Patent Application
20220100989
Techniques exist for performing facial recognition using an image of a person's face. For example, a mobile phone camera may capture an image showing a portion of a person's face. An application may analyze the image to determine characteristics of the person's face and then attempt to match the person's face with other known faces. However, facial recognition is a growing field and various challenges exist related to performing recognition. For example, there are many types of cameras which may capture a variety of images. Sometimes it can be difficult to accurately recognize faces when provided images from different cameras as input. Also, it can be difficult to recognize faces when provided images of faces that are partially covered (e.g., by a face mask, sunglasses, head covering, etc.).
In the following description, various examples will be described. For purposes of explanation, specific configurations and details are set forth in order to provide a thorough understanding of the examples. However, it will also be apparent to one skilled in the art that the examples may be practiced without the specific details. Furthermore, well-known features may be omitted or simplified in order not to obscure the example being described.
Embodiments of the present disclosure can provide techniques for detecting the presence of a particular person when the face of the particular person is partially covered by a face covering. In one example, a computing device may provide a notification if a particular person is detected at the front door of a home. Video or still images of a person wearing a face covering (e.g., a face mask) may be received by the computing device (e.g., at least including a camera). The computing device may utilize a trained model to associate (e.g., to correlate as being a match) one or more of the still images with a reference set of images of the particular person. The reference set of images may contain only images showing the particular person's face that is not covered by a face mask. The model may be configured to associate (e.g., match) the masked (or unmasked) face in the still images with non-covered faces from the reference set of images. The model may also be trained at least in part by utilizing simulated face coverings. By expanding the training data set, using simulated face coverings to include a wider variety and quality of training samples for training the model, and by ensuring that the runtime execution of the model compares masked (or unmasked) faces against a reference set that contains only unmasked faces, embodiments may improve the accuracy of detecting a person's face.
In an illustrative example, consider a scenario in which a resident device within a home environment provides notifications about the presence of a person nearby (or inside) the home. In this example, the resident device may be a home automation device (e.g., a smart speaker, a smart digital media player) that is communicatively connected to a camera. In one example, the camera may be set-up to observe the area around the front door of the home (e.g., to capture images of people who may knock on the door and/or ring the doorbell). Accordingly, the resident device may be configured to receive and process one or more images from the observation camera. The resident device may further be configured to receive and store images (e.g., image croppings) from one or more user devices (e.g., mobile phones). For example, a user device may include a camera component, which may be used for capturing images (e.g., of contacts (e.g., people) associated with a user of the user device). The images may be stored in a local repository (e.g., a local memory repository such as a photo library) of the user device. In some examples, the images captured by the user device camera may have a different level (e.g., a higher level) of quality than images captured by the observation camera (e.g., due to different image resolutions, lighting differences, etc.). Additionally, the images captured by the user device camera may have been taken for the sake of entertainment and/or enjoyment, and are not stored for security purposes or with any intent for being used to recognize people filmed by the observation camera. Thus, these photo library images may be candid scenes, stylized scenes, or the like, and may not always include direct images of people's faces. For example, in some cases, the photo library may include images of people's faces that are partially covered by face coverings, such as a face mask, a hat, a scarf, sunglasses, etc.
Continuing with the above illustration, the resident device may receive one or more image croppings that were selected and generated from the photo library of the user device. The one or more image croppings may, respectively, comprise a portion of a face of a first person who is a contact of the user of the user device. For example, one image cropping may show a side-view of the contact's face, taken in one setting (e.g., a baseball game), while another image cropping may show a front view of the contact's face (e.g., taken as an indoor portrait shot). In this example, it should be understood that there may be many different portions (e.g., views) of the face of the first person that are shown in the images from the photo library, within different contexts. Accordingly, the one or more image croppings may be selected from a larger set of image croppings of the face of the contact based at least in part on determining that the particular set of one or more image croppings results in a greater level of information gain that may be used for subsequent facial recognition of the contact's face. For example, in some embodiments, the user device may determine (e.g., via a trained model) which images in a photo library contain images of faces that are covered (e.g., partially covered) with face coverings. The user device may then determine to not select those images for sending to the resident device, since those images provide a lower level of information gain than images showing face coverings. It should be understood that each image (e.g., non-cropped image) that includes a portion of the face of the first person may also include other objects (e.g., other people's faces, physical objects, environments, etc.). Accordingly, the set of one or more image croppings received from the user device may have been respectively cropped to exclude other objects beyond the portion of the face of the first person.
Upon receiving the one or more image croppings showing non-covered portions of the face of the contact from the user device, the resident device may determine a first set of characteristics of the face of the contact associated with the user device. In one example, the resident device may utilize a trained facial characteristics model (which may also be referred to as a “trained model”) to determine the first set of characteristics based at least in part on the one or more image croppings. In some embodiments, the trained model that executes on the resident device may be the same trained model that also executes on the user device (e.g., that was used to determine which image croppings to send to the resident device), the trained model being deployed to a plurality of devices (including the resident device and the user device) by a remote server. Returning to the trained model executing on the resident device, the trained facial characteristics model may generate a first faceprint for the contact. For example, the first faceprint may correspond to a multidimensional vector (e.g., including 128 dimensions), whereby each dimension is associated with at least one characteristic of the first set of characteristics of the face of the contact.
In some examples, as described further herein, the facial characteristics model may be trained to generate a faceprint based at least in part on images (e.g., and/or image croppings) received from different cameras. For example, the facial characteristics model may receive a first set of training images captured by a camera of the user device (e.g., the mobile phone), and a second set of training images captured by the observation camera. Each training image of each set may include a portion of the face of a person (e.g., the same person). In some examples, the first set of training images may have a different (e.g., higher) level of quality than the second set of training images. As described further herein, the facial characteristics model may be trained to generate a faceprint of the person based at least in part on a difference in quality (and/or a difference in camera source) between the two sets of images.
In some embodiments, the facial characteristics model may also be trained based at least in part on utilizing images showing simulated face coverings, real face coverings, and/or images showing non-covered portions of a face. For example, a remote server may collect images showing different portions of a face of a first person to be used to train the facial characteristics model. Some of the images may show the first person having a face covering (e.g., wearing a face mask), while other images may show the first person's face not covered. The remote server may supplement the training images of the first person by generating simulated face coverings for a portion of the images showing the non-covered face of the first person. The remote server may generated a simulated face covering based in part on identifying facial landmarks on a face (e.g., corners of lips, upper nose boundary, outer eyes, inner eyes, eyebrows, etc.). The facial landmarks may be used to identify a bounding polygon, whereby the remote server may simulate (e.g., render) a facemask over the mouth region according to the parameters of the bounding polygon. In some embodiments, the remote server may vary simulated face masks according to one or more parameters, including, but not limited to, color, texture, and placement of the mask on a face. It should be understood that any suitable combination (e.g., selection) of images (e.g., in terms of image quality and/or inclusion/omission of real or simulated face coverings) may be used to train the model. For example, the model may be trained to associate a first faceprint, generated from a high-quality image showing a non-covered face of a particular person (e.g., captured by a camera of a mobile phone), with a second faceprint, generated from a lower-quality image showing a real covering that covers the face of a particular person (e.g., captured by an observation camera). In another example, the model may be trained to associate a first faceprint, generated from a high-quality image showing a non-covered face of a particular person (e.g., captured by a camera of a mobile phone), with a second faceprint, generated from a high-quality image showing a simulated face covering.
In some embodiments, the trained model may be trained to perform multiple determinations. For example, as described herein, as part of determining a set of characteristics of a face, the trained model may be trained to output a faceprint (e.g., a multi-dimensional vector) of the face. The trained model may also be trained to determine a first level of confidence that a face shown in an image is recognizable (or unrecognizable). For example, the model may utilize a face quality metric that indicates a level of quality associated with a set of characteristics of a face (e.g., represented in part by a faceprint vector). The face quality metric may take into account variables including, for example, blurriness, contrast, lighting, occlusions, etc. In another example, the trained model may be trained to determine a second level of confidence that a face shown in an image is covered by a face covering. These determinations may be used by one or more devices described further herein. For example, as described above, a user device may utilize the trained model to determine which images (e.g., image croppings) to include for sending to a resident device, for inclusion in a reference set of images (e.g., thus excluding images with face coverings). In another example, the resident device may utilize the trained model at runtime to determine whether to include within a notification an image of a person's face that is captured by the observation camera at the front door. The resident device may determine, for example, not to include images that show faces that are masked. In another example, the resident device may include images that are masked within notifications sent to a user device, but may not enable the user device to tag an image to be included in a reference set of images, as described further herein. It should be understood that the trained model may perform other determinations based on the generated faceprint, including, but not limited to, estimating a person's age, gender, whether they are smiling, etc.
Continuing with the above illustration of the resident device in the home environment, at runtime, the resident device may further receive one or more images (e.g., a sequence of video frames) from the observation camera, as described above. For example, a person may approach the front door of the home, whereby the person's face is shown within a viewable area of the observation camera. In this example, the residence device may determine a second set of characteristics associated with the face of the person at the front door based at least in part on the sequence of video frames capturing the person at the front door. For example, the trained facial characteristics model of the resident device may determine a second faceprint of the face of the person at the front door.
The resident device may then determine a score that corresponds to a level of similarity between the first set of characteristics of the face of the first person (e.g., corresponding to the first faceprint, generated from a reference image showing a non-covered face) and the second set of characteristics of the face of the person at the front door (e.g., corresponding to the second faceprint). For example, the resident device may determine the score based on a determined similarity (e.g., a cosine similarity) between the first faceprint and the second faceprint. It should be understood that the resident device may determine the score utilizing any suitable method (e.g., via a sub-model of the trained model, an algorithm for computing vector similarities, etc.). Then, based on the score, the resident device may determine whether the person at the front door is the same as first person (contact). In some embodiments, as described herein, the resident device may utilize a face quality metric to determine if the face of the person at the front door is recognizable or not. For example, in a case where the person's face is turned away from the observation camera, the resident device may be able to identify that a person is in front of the doorway, but may not be able to recognize the face of the person. In a case where the face is determined to be recognizable (e.g., the person's face is facing the camera), then the resident device may then further determine whether the face of the person at the front door matches one of the contacts (e.g., the first person) of the user device.
In some embodiments, the resident device may provide a notification based at least in part on the determination of whether the person at the front door is recognizable/unrecognizable and/or whether the person at the front door matches one of the contacts of the user device (e.g., the first person). For example, continuing with the illustration above, in the event that the resident device determines that the person at the front door is recognizable and is the first person, the resident device (e.g., a smart speaker) may provide the notification via an audio signal, announcing that the contact has arrived at the home. In another example, the resident device may provide the notification to a user device (e.g., a mobile phone) via a message that includes text, audio, images, video, or any suitable combination thereof. In another example, the resident device may be configured to announce only when non-contacts have arrived (and otherwise remain silent). In this example, upon determining that the person at the front door is not a known contact (e.g., not the first person), the resident device may announce that a person who is not a contact has arrived. As described further herein, other channels and/or conditions for providing notifications may be utilized.
In some embodiments, the resident device may provide within (and/or alongside) the notification an image that corresponds to the highest quality image determined among the sequence of frames (e.g., images) received from the observation camera. For example, as described above, the resident device may receive the sequence of frames (e.g., from a video camera) that respectively capture the same person approaching the front door. Each frame may have different levels of quality compared to other frames. For example, one frame might show a person's face from a side angle instead of a straight on view. Another frame might also show the same person's face, but has an image artifact, poor lighting quality, or other characteristic that degrades image quality. The resident device may determine, for each frame showing the person's face, a face quality score based in part on the face quality metric, as described herein. The resident device may then sort the scores and select the image that has the highest face quality score. The resident device may then provide to a user device (e.g., as part of the notification) the highest quality image showing the face of the person at the front door. The user device may then present the image on a display (e.g., a mobile phone display, a TV display, etc.) for visual identification. In this way, the user may be presented with the highest quality image of the person's face, which may provide a better user experience.
In some embodiments, an image provided as part of a notification to a user device may also be tagged by the user device upon receiving input by a user. For example, suppose that the resident device detects a person at the front door, and determines that the person is not one of the known contacts of the user (e.g., there are no images of the person in the user's photo library). In this case, upon receiving the notification, the user device may present the user with an opportunity to tag the photo, for example, in case the user recognizes the person at the door as a contact of the user. Upon receiving input to tag (e.g., label the photo), the user device may add the photo to a reference set of images that may be used for future detection of the person, as described further herein. It should be understood that, although embodiments described herein may primarily refer to scenarios in which reference images (e.g., used to perform facial recognition) are received from user devices (e.g., mobile phones), embodiments should not be construed to be so limited. For example, an acceptable quality image (e.g., of a non-covered face) captured by an observation camera (e.g., that is associated with the resident device in the home environment) may be tagged as a contact person and later included in a set of reference images of the contact person. A faceprint may be generated for the newly tagged photo within the reference set, and the trained facial characteristics model may associate the faceprint with faceprints of other (e.g., later received) photos of the same person's face (e.g., whether the face is covered or not covered).
In some embodiments, although a face may be recognized even with the presence of a face mask, the image of the face with the face mask may not be included within a notification sent to the user device. In some embodiments, the image of the face with the face mask may be included within the notification, but the user may not be given the opportunity to subsequently tag the image. In some embodiments, the user may be given the opportunity to tag the masked image, but the trained model of the user device may determine not to transmit an image cropping of the masked face to the resident device. In some embodiments, this may ensure that images within facemasks are not included in a corpus of images used to generate a reference set of images (e.g., which may otherwise degrade the quality of facial recognition performed by the facial characteristics model).
The embodiments of the present disclosure provide several technical advantages over existing systems. In one example, embodiments of the present disclosure enable a system to perform facial recognition of future images based on images that are already existing and tagged (e.g., assigned to a contact) within in a user's personal photo library on their user device (e.g., mobile phone). This may reduce resource consumption by obviating a need for users to take one or more photos (e.g., images) for the purpose of performing future facial recognition. In another example, embodiments of the present disclosure may reduce the amount of network bandwidth, storage, and processing resources required. For example, instead of a resident device receiving and processing a large amount of photos from a user's photo library on their mobile device (e.g., for later use in facial recognition), the resident device may instead only receive a subset of photos of the photo library, whereby the subset has been selected according to which photos provide a higher level of information gain. In another example, embodiments of the present disclosure enable systems to perform facial recognition, whereby a set of images used as reference images for facial recognition may be different than a set of images that is used to perform the actual facial recognition. For example, a set of reference images may be captured by a user's mobile device (e.g., and/or shared by another mobile device with the user's mobile device) and then shared with the resident device. Later, the resident device may perform the actual facial recognition using images received from a separate observation camera, which may generate images having a different level of quality (e.g., lower resolution, more noise, more image artifacts, etc.) than the set of reference images. In this way, embodiments of the present disclosure enable a method whereby existing photos generated from one camera (e.g., an existing photo library of a mobile phone) may be used to perform, with high accuracy, facial recognition using photos generated from another camera (e.g., an observation camera) as input.
In another example of a technical advantage, embodiments of the present disclosure enable a system to perform more accurate facial recognition on images that include partially covered faces of persons. For example, as described herein, a facial characteristics model may be trained based at least in part on a generation of simulated face coverings. These simulated face coverings may be rendered on a portion of a face shown in an image, whereby a faceprint is then generated based on the modified image. The model may then be trained to associate the faceprint from the modified image (including the simulated face covering) with a faceprint generated from another image that shows a non-covered face of the same person (e.g., increasing the cosine similarity between faceprint vectors of the same face (masked and unmasked)). By utilizing a variety of simulated face coverings as part of the training process, embodiments may not only improve the efficiency of the training process (e.g., reducing the time needed to gather images showing images with faces wearing real face masks), but also may improve the accuracy of the trained model in detecting a particular person (e.g., having been trained based on a variety of different simulated face mask configurations). It should be understood that the training process may include matching any suitable combination of image types (e.g., including images showing real mask coverings, simulated mask coverings, non-covered faces, etc.).
Additionally, embodiments may also enable a more efficient and accurate runtime detection of a particular person by ensuring that a set of reference images used to perform the runtime facial detection includes only suitable quality images. For example, as described herein, a trained model executing on a user device may determine to only transmit images (e.g., image croppings) of images showing substantially non-covered portions of faces (e.g., from a photo library). Accordingly, a resident device that receives an image from a user device, may determine to include the image in a reference set of images based on the determination (e.g., by the user device and/or a trained model of the resident device) that the portion of the face shown in the image is not covered by a face covering. In another example, the resident device may ensure that images received from another device (e.g., an observation camera), and subsequently tagged by a user as being a contact, are only included in a reference set of images if they match a threshold level of face quality (e.g., according to a face quality metric and/or ensuring that the portion of the faces shown in the images are not covered). By ensuring an acceptable level of quality of images in the set of reference images, embodiments improve accuracy when performing facial recognition against other images (e.g., lower quality images, images showing masked faces, etc.). This also enables the trained model to perform facial recognition on new faces (e.g., which were not used as training samples to train the model), and still ensure an acceptable level of accuracy when performing facial recognition.
For clarity of illustration, it should be understood that, although embodiments of the present disclosure are primarily directed to performing facial recognition of a person's face, embodiments should not be construed to be so limited. For example, a system of the present disclosure may be trained to recognize any suitable type of object, and then take suitable action accordingly (e.g., providing notifications, granting access, etc.). In one example, a person's hand may contain unique characteristics that may be associated with a person's contact. The system may be trained to recognize hands from images captured by one camera, while using images captured by another camera as reference images. Additionally, although embodiments of the present disclosure may primarily discuss performing facial recognition on partially covered faces involving a face mask, embodiments should not be construed to be so limited. For example, a system of the present disclosure may be trained to generate any suitable type of simulated face covering for using in training a facial characteristics model. Also, the system may also determine to exclude (and/or include) images having any suitable type of face covering (e.g., sunglasses, hats, etc.).
Turning to the elements of
In some embodiments, the resident device 102 may be any suitable computing device that resides in a particular environment and is configured to control (e.g., provide control instructions) one or more operations and/or accessories in the environment. In some non-limiting examples, a resident device may be a smart speaker, a smart TV device, a tablet device, a smart digital media player (e.g., configured to provide streaming media to a TV), etc. In the example of
In some embodiments, the resident device 102 may be communicatively connected to any suitable camera. For example, as depicted in
Although
As discussed above, the resident device 102 and/or user devices (e.g., 108, 112) may be configured according to one or more control settings. For example, in some embodiments, a user device may receive input from a user for determining whether the photo library of the user device should be shared with other users (e.g., in the home). In one case, user device 108 may share the photo library with user device 112. In this case, any contacts that are determined from images from the photo library of user device 108 may also be considered as contacts associated with user device 112. Accordingly, although in some embodiments, a “photo library” (e.g., also known as a “library of images”) may correspond to only images that are stored on a local device, in other embodiments, a “photo library” may correspond to a collection of photos that are shared across multiple user devices. In some embodiments, the user device may also be configured to determine whether to receive (or incorporate) images that are shared from one or more other user devices (e.g., of family members that have shared their respective photo libraries). In some embodiments, the user device may be configured to automatically tag (e.g., label) images that are determined to contain a face of an existing contact associated with the user device (and/or the user). For example, user device 108 may receive an input that tags a person's face in an image of the photo library (e.g., captured by a camera component of user device 108) as being person 120. Thereafter, the user device 108 may automatically tag users captured by the camera of the user device 108 as being person 120. In another example, the user device 108 may present an image captured by observation camera 122 showing person 120. The user device 108 may receive input from user 106 to label the image as showing person 120, which may be later used for facial recognition, as described herein. In yet another example, the user device 108 may not receive an input from a user to tag someone in an image as being a particular known contact. However, the user device 108 may nevertheless group images together that contain the same face. In some cases, the user device 108 may associate any faces detected in photos in the photo library as being a known contact (e.g., even if an explicit contact name is not assigned).
In some embodiments, as described further herein, a user device may enable a user to tag a photo (e.g., received from an observation camera) based in part on the quality of the photo (e.g., the portion of the face) captured by the observation camera. For example, suppose that the observation camera 122 captures an image of the person 120 wearing a face mask, as depicted in
In some embodiments, a resident device may be configured to provide notifications based at least in part on the type of person recognized. For example, in one embodiment, resident device 102 may receive a request from a user device 108 to only receive notifications when a person is detected who is not a contact associated with user device 108 (e.g., the person is not found in any of the images of the photo library of user device 108). This setting may be used for example, when user 106 only wants to be notified when non-contacts approach the home, but not when relatives are coming home. In another embodiment, resident device 102 may receive a request from a user device 108 to only receive notifications when a person is detected who is a contact associated with user device 108. In another embodiment, the resident device 102 may be configured to provide a notification when any person is detected, whether or not the person is recognizable or unrecognizable. It should be understood that the above described settings are only representative, and any suitable types of settings may be used to configure a resident device and/or user device. In some cases, a particular setting may result in an increase in the number of notifications provided by a resident device (e.g., configuring the resident device to notify a user whenever any person is detected). In some cases, a particular setting may result in a decrease in the number of notifications provided by a resident device (e.g., configuring the resident device 102 to only provide notifications if a person is detected that matches the photo library on the particular user device). In some embodiments, a notification may contain any suitable information beyond the identity of a person that is detected. For example, the resident device 102 may be configured to provide a notification to user device 108 that indicates that person 120 (e.g., a child of user 106) arrived home at a particular time.
In some embodiments, as described above, the images in a photo library of a user device may be received by the user device from any suitable source. Using user device 202 as an example, in one embodiment, the user device 202 may capture an image using a camera component of the user device 202 (e.g., a mobile phone). Upon capturing the image, the user device 202 may store the image in the photo library 210. In another embodiment, the user device 202 may receive one or more images from another device. For example, the user device 202 may directly receive and store images shared from other device (e.g., user device 204, or another device). Note that in some cases, both user devices 202 and 204 may share each other's libraries with one another, even though they don't respectively directly transfer images to the other device. For example, as described above, the photo libraries on each device may be made available (e.g., shared) so that the resident device 206 has access to both libraries. In this case, the resident device may be responsible for managing (e.g., and/or synchronizing) a shared photo library for at least the purposes of providing notifications. As described further herein, this shared photo library that is managed by the resident device 206 may be a subset of photos (e.g., selected image croppings) of respective photo libraries stored in local memory repositories on the user devices 202, 204. These selected image croppings may be included in one or more sets of reference images for respective faces of different people, which the resident device 206 may use to perform facial recognition.
In some embodiments, each image in a photo library (e.g., photo library 210 or 230) may have a particular level of quality. A level of quality of an image may be associated with one or more factors. In some non-limiting examples, the level of quality may be associated with a level of distortion (e.g., radial distortion) in the image, an image resolution (e.g., represented by a number of pixels (or pixels per inch (ppi)), a level of image contrast (e.g., a contrast ratio), an image sharpness, and/or other variables. For example, one variable might correspond to whether there are any occlusions in the picture that block (or obstruct) a face of a person being recognized. Another variable might correspond to whether there are any image artifacts, for example due to image compression or other noise that may be included in an image. It should be understood that, in some examples, different cameras may be associated with different levels of image quality. For example, the camera components of user devices 202 and 204 may be associated with higher levels of image quality than an observation camera (e.g., observation camera 122 of
In some embodiments, images in photo libraries of user devices may capture faces of people, whereby a face in an image may be partially covered by a face covering. For example, as described further below with respect to
Turning to the contents of each of the photo libraries in further detail, as depicted in
As described above, in some embodiments, a trained facial characteristics model may be executed on each user device for use in determining which images (e.g., image croppings) should be transmitted to the resident device 206. These image croppings may later be used by the resident device 206 as reference images for performing facial recognition. In some embodiments, the trained facial characteristics model that is executed on a user device (e.g., 202, 204) may be similar to (e.g., the same) the facial characteristics model 132 of the notification service 130 that executes on the resident device 102, although the model may be used for different purpose. For example, as described herein, the facial characteristics model executing on a user device may be used to generated faceprints from image croppings. Then, based on comparing the generated faceprints to determine a level of information gain from each faceprint, the user device may determine to transmit a subset of the image croppings as reference images for a particular face to the resident device 206. Meanwhile, a similar (e.g., same) facial characteristics model executing on the resident device 206 may be used to generate faceprints for later comparison when determining a score for performing facial recognition. In some embodiments, the trained facial characteristics model may be trained as described herein, for example, in reference to
In the simplified diagram depicted in
A similar analysis may be performed for the second contact person. In one example, the user device 202 may determine, based on comparing faceprints for each image cropping, that image croppings 218, 222, and 224 each show different facial views of the second contact person, so that a high level of information gain is obtained from each image. In this example, each of the image croppings may then be included in a subset of image croppings of the second contact person that is transmitted to the resident device 206. It should be understood that any suitable size subset of image croppings for a given contact may be determined by a user device (e.g., the user device 202, 204). For example, in an example, where there may be 20 image croppings of the first contact person (e.g., pictured in image 212) in the photo library 210, the user device 202 may select the 10 best image croppings that are determined to provide the most amount of information gain.
As described earlier, in the example depicted in
In some embodiments, the resident device 206 may receive the subset of image croppings for each contact (e.g., the first and second contact person) from each user device (202, 204). Upon receiving each subset of image croppings, the resident device 206 may further combine subsets that match the same contact person to thereby determine a set of reference images (e.g., image croppings) for the particular contact person. For example, the resident device may follow a similar procedure as described above to determine which image croppings (from the combined subsets for the particular contact person) to include. This may involve selecting image croppings that provide a higher level of information gain. In some embodiments, this may involve a facial characteristics model of the resident device 206 first generating faceprints for each image cropping, comparing the faceprints, and then determining (e.g., selecting) a set of reference images to use. In some embodiments, the trained facial characteristics model of the resident device 206 may further determine if any of the image croppings show a face covered with a face covering (e.g., in an event where a user device transmitted an image cropping showing a covered face). In this example, the resident device may determine to exclude the image cropping based at least in part on this determination that the image cropping shows a face covering over a face. In another embodiment, the resident device 206 may still include the image cropping showing a face covering that partially covers a face, for example, if the level of information gain provided by the image cropping was sufficient to warrant inclusion into the set of reference images. The resident device 206 may then store the set of reference images for each contact person that is received across the different user devices to a local memory of the resident device 206. In this way, the resident device 206 may coordinate (e.g., synchronize) contacts across different user devices and facilitate a shared library between the different user devices. In some embodiments, the resident device 206 may also (or alternatively) store faceprints that correspond to each of the images of a set of reference images for a particular contact person, to be later used for facial recognition (e.g., as described in reference to
In some embodiments, the resident device 206 may receive new image croppings that may be used to update one or more sets of reference images of contact persons. For example, the user device 202 may capture one or more new images of the first contact person (e.g., depicted in image 212). The one or more new images may capture unique portions (and/or may have a higher level of quality) than previous images of the first contact person that are stored in the photo library 210. Accordingly, the user device 202 may determine that the one or more new images provide an additional level of information gain than one or more images from the existing set of reference images. A service may execute on the user device 202 (e.g., executing on any suitable cadence) to determine whether a new image cropping should be sent to the resident device 206 for updating the set of reference images for the first contact person. It should be understood that the resident device 206, as it may receive one or more new images from multiple user devices, may also coordinate updates of reference images across multiple user devices. For example, the resident device 206 may determine what additional information gain may be provided by each new image from each device. In some embodiments, the resident device 206 may generate one or more updated faceprints for new images that are added to generate an updated reference set of reference images for a particular contact person. In at least this way, the resident device 206 may continuously improve the quality (e.g., and/or coverage) of the set of reference images for a particular contact person.
It should be understood that, although the embodiments depicted by
Turning to the process 300 in further detail, at block 302, the system may determine a first set of characteristics of a non-covered face of a first person based on an image received from a first camera. To perform this determination, and, using diagram 301 for illustration, the system may first receive one or more images (e.g., the plurality of image croppings 311), from one or more user devices (e.g., user device 202 and/or user device 204 of
At block 304, the system may determine a second set of characteristics of a face of a second person (e.g., person 317, whose identity has not yet been determined) based on an image (e.g., image 319) received from a second camera (e.g., camera 315). Continuing with diagram 301 for illustration, and, as described herein, the camera 315 may be connected to the system (e.g., the resident device). In an example, the camera 315 may be positioned (e.g., mounted) at a particular location (e.g., outside the doorway to a home), whereby the system may be located inside the home and connected to the camera 315 via a wired (or wireless) connection. The camera 315 may capture a video feed of the person 317 approaching the doorway of the home. The system may receive a plurality of images from the camera 315, which may correspond to a sequence of video frames of the video feed of the camera 315. Image 319 may be one of the plurality of images from the video feed. In this example, the second person is wearing a face mask. Accordingly, the set of images received from the camera 315 respectively shows portions of the face that is partially covered. The facial characteristics model of the system may generate the second set of characteristics 321 based on the image 319. For example, similar to as described above, the facial characteristics model may generate a faceprint of the face of person 317 based on the image 319. In this case, the generated faceprint may correspond to the second set of characteristics 321. It should be understood that, although only a single image (e.g., image 319) of the plurality of images of the video feed is being described in this illustration, the system may generate faceprints for any one or more of the plurality of images received from the camera 315. In some cases, one or more of these faceprints may provide more information gain than others for detecting the face of person 317. For example, in one time interval, a video frame may capture the person 317 facing away from the camera (e.g., providing less information gain), while in another time interval, the person's face may be facing toward (or at a side view from) the camera, and/or the person may have removed their face mask. In this example, note that the faceprint generated from image 319 incorporates the fact that the face of the person 317 is partially covered by the face mask. For example, as described further in reference to
At block 306, the system may determine that the first person is the second person by comparing the first set of characteristics with the second set of characteristics. Using diagram 301 for illustration, the system may compare the first set of characteristics 313 with the second set of characteristics 321. For example, the system may compare one or more faceprints that correspond to the first set of characteristics 313 with one or more faceprints that correspond to the second set of characteristics 321. For example, consider a case where one of the plurality of image croppings 311 shows a side view of the first person contact. In this case, suppose also that image 319 shows a side view of the second person 317. The system may compare faceprints that respectively correspond to each of these images to determine a level of similarity between the faceprints. As described further herein (e.g., with respect to
As described herein, to train the model to accurately compare faceprints from partially covered faces with faceprints from non-covered faces, embodiments may utilize simulated face coverings. For example, as described further below, a system used to train the model may receive one or more images showing portions of non-covered faces. The system may then determine (e.g., via a machine learning model such as a neural network) a set of facial landmarks for each non-covered face. The facial landmarks may be used to identify a bounding polygon that defines parameters (e.g., shape parameters) for the simulated face mask. The system may then render (e.g., rasterizing using an suitable rendering algorithm) a simulated face covering (e.g., a face mask) to cover a portion of the originally non-covered face according to the determined bounding polygon. For example, the system may render a simulated face mask to cover a person's lips and nose. As described herein, the generated simulated face masks may vary according to one or more factors, including, but not limited to, a mask color, a mask texture, a size of the mask, a shape of the mask, or a placement of a face mask on a face.
Accordingly, in some embodiments, the process of training the facial characteristics model may include utilizing a wide variety of training sample data. For example, the training sample data may include images showing portions of non-covered faces. It may also include images showing portions of partially covered faces, whereby the face coverings are simulated and generated from images that originally show non-covered faces. The training sample data may also include images showing real face coverings that partially cover faces. In this way, embodiments enable training to be performed across a wider variety of training data than may otherwise be achieve by utilizing only original images. For example, the model may be trained to associate faceprints generated from non-covered faces with faceprints generated from partially covered faces (e.g., either real or simulated face coverings). This may improve the overall detection accuracy of the trained model. It should be understood that the existence of a face covering (e.g., a face mask) is one variable of a plurality of variables that may increase the variety of images in the training data set. For example, as described below, the model may be trained utilizing images of different level of quality (e.g., resolution, contrast, noise, etc.), such that the model may be trained to match a person's face shown in a low quality image with the same person's face in a higher quality image.
Turning to
Meanwhile, an observation camera 403 of
In some embodiments, a notification service 410 may be similar to notification service 130 of
In some embodiments, the training process may begin whereby an untrained facial characteristics model 430 receives a plurality of images 402 (e.g., image croppings) of a person (e.g., person 1). This plurality of images 402 may, respectively, include different portions of a face of the person. For example, one portion may be a side view, another portion may be a straight-on view, another portion may be an opposite side view, etc. Some portions may have different conditions and/or backgrounds. In some embodiments, the plurality of images 402 may be captured by the user device 401 at the first level of image quality (or similar levels of quality). As depicted in
In some embodiments, upon receiving both pluralities of images 402 and 420, the system may train the facial characteristics model 430 using a cross-recognition training algorithm. In some embodiments, the cross-recognition training algorithm may utilize any suitable machine learning technique. Some non-limiting examples may include utilizing a neural network, support vector machines, nearest neighbor approach, or decision trees. In one example involving a neural network, the neural network may receive input corresponding to one of the images. In some embodiments, the input may correspond to one or more features of the image. For example, the image may be composed of multiple features (e.g., pixel blocks of an image), which are received by the neural network. The neural network may have one or more layers of nodes (e.g., an input layer, a hidden layer, and/or an output layer). Each node of a layer may represent an element of information. The generated prediction model may include a number of interconnections between the hidden layers and the input layer and/or output layer (e.g., between nodes of the different layers), each of which may be assigned a numeric weight generated based on a pattern identified between the set of input values and the set of output values. The weight may be tuned (e.g., based on a training dataset), rendering the artificial neural network adaptive to inputs and capable of learning. Generally, the hidden layer(s) allows knowledge about the input nodes of the input layer to be shared among the output nodes of the output layer. To do so, a transformation f is applied to the input nodes through the hidden layer. The artificial neural network may also use a cost function to find an optimal solution (e.g., an optimal transformation function). The optimal solution represents the situation where no solution has a cost less than the cost of the optimal solution. In an example, the cost function includes a mean-squared error function that minimizes the average squared error between an output f (x) (e.g., a prediction, given training data input x) and a target value y (e.g., a ground truth value) over the example pairs (x, y). In some embodiments, a backpropagation algorithm that uses gradient descent to minimize the cost function may be used to train the artificial neural network. In this example, one or more parameters (e.g., which also may be known as “hyperparameters”) may be used to administer the training process. For example, these parameters may include determining how many hidden layers of nodes to use between the input layer and the output layer, and how many nodes each layer should use. In this example, the collection of nodes and determined weights (e.g., based on training data) between interconnections of nodes between the different layers may form the trained model.
Continuing with the training process example of
In some embodiments, the facial characteristics model 430 may be trained based at least in part on receiving different sets of pluralities of images associated with multiple persons. For example, similar to as described above in reference to “person 1,” the facial characteristics model 430 may receive a plurality of images 404 (e.g., image croppings) of another person (e.g., person 2). This plurality of images 402 may, respectively, include different portions of a face of the person. The facial characteristics model 430 may also receive a plurality of images 422 of the same person (e.g., person 2). This plurality of images 422 may have been captured by the observation camera 403 at the second level of image quality (or similar levels of quality). The facial characteristics model 430 may utilize a similar technique as described above to train the facial characteristics model 430 to generate a set of “person 2 faceprints” 434. It should be understood that, similar to the plurality of images 402 and 420, each of the sets of pluralities of images may including one or more images showing portions of faces that may (or may not) be covered by a face covering (e.g., a simulated face covering or real face covering). Accordingly, the facial characteristics model 430 may receive training samples associated with any suitable number of persons (e.g., several hundred, thousand, etc.), and produce faceprints for each person (e.g., generating “person N faceprints” 436 based on receiving a plurality of images 406 (e.g., of person N) and another plurality of images 424 (e.g., also of person N).
As described herein, it should be understood that one of the advantages of embodiments of the present disclosure is that the system may perform facial recognition using images that may have different camera sources and/or differing levels of quality. For example, a set of reference images may be primarily captured by a user's mobile phone. These reference images may typically have a higher level of quality than images captured by an observation camera. Embodiments of the present disclosure effectively “bridge the gap” by allowing the system to recognize faces in images of lower quality by comparing with faces in images of higher quality. As described in reference to
In some embodiments, once trained, the trained facial characteristics model 430 may be used to generate faceprints for a face of any suitable person (e.g., including faces of persons that were not part of the training samples for training the facial characteristics model 430). In some embodiments, the trained facial characteristics model 430 may be deployed to be executed in any suitable environment. For example, as described herein, the trained facial characteristics model 430 may execute on a user device (e.g., user device 202 or 204 of
In some embodiments, the process for training a facial characteristics model and/or updating a trained model may be sub-divided into components. For example, a remote server may be responsible for an initial training of a model that utilizes a variety of images to train the model. The remote server may then deploy the model to a plurality of user devices and/or resident devices. In some embodiments, the trained model may be further trained based on images from the specific set of images corresponding to contacts for a particular user device and/or resident device (e.g., of a home environment). For example, a resident device may be configured to run a new training process that incorporates images from one or more photo libraries of the home environment associated with the particular resident device. In this way, the model may be further “fine-tuned” to more accurately recognize a specific set of faces. In some embodiments, any further training that is performed locally on individual user or resident devices may be shared with a central remote server. For example, in one example, each user device and/or resident device of a plurality of devices may periodically transmit back to a remote server a data set that represents differences from the original trained model (e.g., deployed by the remote server to the various devices) and the respective updated trained models on each device. The remote server may then incorporate these differences into an updated centralized trained model (e.g., as part of a checkpoint process, to align the various models). This updated trained model may then be re-deployed to various devices. It should be understood that the model generation and update process may be performed by any suitable combination of devices and/or sub-processes.
The network 508 may include any suitable communication path or channel such as, for instance, a wire or cable, fiber optics, a telephone line, a cellular link, a radio frequency (RF) link, a WAN or LAN network, the Internet, or any other suitable medium. The network 508 may include any one or a combination of many different types of networks, such as cable networks, the Internet, wireless networks, cellular networks, and other private and/or public networks.
Turning to each element in further detail, the user device 502 may be any suitable computing device (e.g., a mobile phone, tablet, personal computer (PC), smart glasses, a smart watch, etc.). In some embodiments, the user device 502 will have a camera embedded as a component of the device (e.g., a mobile phone camera). In some embodiments, the user device 502 will be connected to another device (e.g., a standalone digital camera), from which it receives images (e.g., over the network 508). The user device 502 has at least one memory 510, one or more processing units (or processor(s)) 514, a storage unit 516, a communications interface 518, and an input/output (I/O) device(s) 520.
The processor(s) 514 may be implemented as appropriate in hardware, computer-executable instructions, firmware or combinations thereof. Computer-executable instruction or firmware implementations of the processor(s) 514 may include computer-executable or machine executable instructions written in any suitable programming language to perform the various functions described.
The memory 510 may store program instructions that are loadable and executable on the processor(s) 514, as well as data generated during the execution of these programs. Depending on the configuration and type of user device 502, the memory 510 may be volatile (such as random access memory (RAM)) and/or non-volatile (such as read-only memory (ROM), flash memory, etc.). In some implementations, the memory 510 may include multiple different types of memory, such as static random access memory (SRAM), dynamic random access memory (DRAM) or ROM. The user device 502 may also include additional storage 516, such as either removable storage or non-removable storage including, but not limited to, magnetic storage, optical disks, and/or tape storage. The disk drives and their associated computer-readable media may provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for the computing devices. In some embodiments, the storage 516 may be utilized to storage a photo library containing one or more images on the user device 502.
The user device 502 may also contain the communications interface 518 that allow the user device 502 to communicate with a stored database, another computing device or server, user terminals, and/or other devices on the network(s) 508. The user device 502 may also include I/O device(s) 520, such as for enabling connection with a keyboard, a mouse, a pen, a voice input device, a touch input device, a display, speakers, a printer, etc.
Turning to the contents of the memory 510 in more detail, the memory 510 may include an operating system and one or more application programs or services for implementing the features disclosed herein, including a notification management module 512. The notification management module 512 may be responsible for performing one or more tasks, including configuring a notification service of the resident device 506 and/or sending (and/or receiving) data (e.g., image croppings) to the resident device 506. For example, as described herein, the notification management module 512 may receive input for configuring the resident device with settings for providing notifications. As described herein, one example may be a setting that indicates that the resident device 506 should only provide a notification to the user device 502 if a person is detected that is (or is not) a contact associated with the user device (e.g., the person is not found in any of the images in the photo library in storage 516). In another example, a setting may indicate how a notification should be provided. For example, one setting may indicate that the resident device 506 should transmit a notification message to the user device 502. Another setting may indicated that the resident device should announce a notification on a speaker connected to the resident device 506. The notification management module 512 (e.g., via a service or background application running on the user device 502) may transmit images (e.g., image croppings generated from the photo library) to the resident device 506 for processing by the resident device 506. These images may be transmitted on any suitable cadence and/or selection algorithm, for example, as described in reference to
In some embodiments, the observation camera 504 may correspond to any suitable camera for capturing and transmitting images to the resident device 506. In some embodiments, the observation camera 504 may be positioned (e.g., mounted) at a particular location to have a particular viewable area, for example, near the front door of a home. The observation camera 504 may be connected to the resident device 506 via network 508.
In some embodiments, as described above the remote server 522 may correspond to a cloud computing platform. The remote server 522 may perform one or more functions, including, for example: training one or more facial characteristics models (e.g., similar to as described in reference to
Turning to the resident device 506 in further detail, the resident device 506 may be a computer system that comprises at least one memory 530, one or more processing units (or processor(s)) 546, a storage unit 548, a communication device 550, and an I/O device 552. In some embodiments, these elements may be implemented similarly (or differently) than as described in reference to similar elements of user device 502. In some embodiments, the storage unit 548 may store images (e.g., image croppings) received by user device 502 and/or remote server 522. The resident device 506 may be housed in any suitable unit (e.g., a smart TV, a smart speaker, etc.).
Turning to the contents of the memory 530 in more detail, the memory 530 may include an operating system 532 and one or more application programs or services for implementing the features disclosed herein, including a communications module 534, an encryption module 536, a notification management module 538, a profile management module 540, a scoring module 542, and a model training module 544. In some embodiments, one or more application programs or services of memory 530 may be included as part of the notification service 130 of
The communications module 534 may comprise code that causes the processor 546 to generate messages, forward messages, reformat messages, and/or otherwise communicate with other entities. For example, the communications module 534 may receive (and/or transmit) images from the user device 502 and/or remote server 522. The communications module 534 may also be responsible for providing notifications. For example, the communications module 534 may transmit a notification message to the user device 502 upon detecting the presence of a person based on an image received from observation camera 504. In some embodiments, the communications module 534 may provide a notification using any suitable channel and/or to any suitable device. For example, the communications module 534 may provide an audible notification via a speaker I/O device 552 at a location within a home environment. In another example, the communications module 534 may provide an audiovisual notification to a smart TV within a home environment. For example, a PIP display of the smart TV may display a video feed from camera 504 (e.g., showing a user at the front door of a home). The smart TV may also announce who is at the door and/or allow two-way communication via a speaker and/or microphone I/O devices of the resident device 506.
The encryption module 536 may comprise code that causes the processor 546 to encrypt and/or decrypt messages. For example, the encryption module 536 may receive encrypted data (e.g., an encrypted image cropping) from the remote server 522. The encryption module 536 may include any suitable encryption algorithms to encrypt data in embodiments of the invention. Suitable data encryption algorithms may include Data Encryption Standard (DES), tripe DES, Advanced Encryption Standard (AES), etc. It may also store (e.g., in storage unit 548) encryption keys (e.g., encryption and/or decryption keys) that can be used with such encryption algorithms. The encryption module 536 may utilize symmetric or asymmetric encryption techniques to encrypt and/or verify data. For example, as noted above, the user device 502 may contain similar code and/or keys as encryption module 536 that is suitable for encrypting/decrypting data communications with the resident device (and/or remote server 522).
The notification management module 538 may comprise code that causes the processor 546 to store and manage settings for providing notifications, as described herein. The notification management module 538 may also be responsible for generating notifications that are provided by the communications module 534. It should be understood that a notification may presented in any suitable form (e.g., text, audio, video, and/or suitable combinations). In some embodiments, the notification management module 538 may be configured to performing no operation (e.g., a “no-op”) in a particular setting. For example, the resident device 506 may be configured to only provide AV-based notifications to user device 502 if a detected person is not a contact. Accordingly, if the resident device 506 detects a contact person, the notification management module 538 may determine to perform no operation (e.g., remaining silent, only logging the observation internally, etc.). In some embodiments, the notification management module 538 may also determine whether to provide notifications based on whether a face is recognizable or not. In some embodiments, the notification management module 538 may determine what data to include in a notification and/or what functions to enable based in part on the results of performing facial recognition on an image. For example, the notification management module 538 may determine that, if a portion of a face shown in an image is partially covered, then the image should not be included in the notification that is transmitted to a user device for presentation. In this way, the resident device 506 may prevent the image from being tagged to be included in a set of reference images for the particular person. In another example, the notification management module 538 may determine to include the image within the notification. However, the notification may be provided so that, although the receiving user device (e.g., user device 502) may present the image to a user for visualization, the user is not able to tag the photo.
The profile management module 540 may comprise code that causes the processor 546 to maintain and store profiles of contacts. For example, as described herein (e.g., in reference to
The scoring module 542 may comprise code that causes the processor 546 to determine a score that corresponds to a level of similarity between a first set of characteristics (e.g., associated with a face of a first person) and a second set of characteristics (e.g., associated with a face of a second person). In some embodiments, the scoring module 542 may utilize a trained facial characteristics model to generate a faceprint (and/or multiple faceprints) based on one or more reference images (e.g., image croppings) of the first person. The facial characteristics model may also generate a faceprint (and/or multiple faceprints) based on one or more images received from the camera 504 (e.g., showing the face of the second person). The system may then compare faceprints (e.g., determining a similarity between faceprint vectors) to determine the score. In some embodiments, the resident device 506 may utilize the score to determine whether the first person is the second person. In this way, the resident device 506 may determine whether or not to provide a notification, and, if so, what type of notification to provide. In some embodiments, the first set of characteristics may correspond to the faceprint(s) of the first person (e.g., generated from a set of one or more reference images), and the second set of characteristics may correspond to the faceprint(s) of the second person (e.g., generated based on the image(s) received from observation camera 504). In some embodiments, as described herein, the scoring module 542 may also determine, based on a face quality metric, whether or not the face of the second person is recognizable or not recognizable. In some embodiments, the system may first determine that a face is recognizable before determining if the face is a known contact. For example, a face may be considered recognizable if the set of facial characteristics of an image (e.g., received from observation camera 504) are of a sufficient quality (e.g., matching a predefined threshold) so that the system may thereafter accurately determine if the person is a known contact or not. In some embodiments, as described herein, the scoring module 542 may also determine one or more data points may also correspond to the set of characteristics of a particular person. In some embodiments, these data points may be generated (e.g., derived) based in part on the faceprint that is generated from the respective image. Some non-limiting examples of data points include the gender of the person shown in the image, the age of the person, whether the face is partially covered or not, a particular facial expression (e.g., smiling, frowning, etc.), etc. It should be understood that, for each data point, the system may determine a level of confidence as to the likelihood of the prediction, for example, a likelihood of whether the image shows a partially covered face. One or more of these data points, along with the underlying faceprint, may be used by the system to perform facial recognition and/or determine the contents of a notification message (e.g., whether to include an image in the notification).
The model training module 544 may comprise code that causes the processor 546 to train a facial characteristics model. In some embodiments, the operations of model training module 544 may be similar to as described in reference to
Additionally, some, any, or all of the processes may be performed under the control of one or more computer systems configured with executable instructions and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) executing collectively on one or more processors, by hardware, or combinations thereof. As noted above, the code may be stored on a computer-readable storage medium, for example, in the form of a computer program comprising a plurality of instructions executable by one or more processors. The computer-readable storage medium is non-transitory.
In some embodiments, process 600 may be performed by a first device (e.g., a resident device), which may correspond to any one or more of the resident devices described herein. It should be understood that, in some embodiments, the first device may also correspond to another type of device, including a user device or remote server, as described herein. At block 602, the resident device may receive (e.g., from a user device) a first image (e.g., an image cropping of an image) showing a portion of a face of a first person. In some embodiments, the first image may have been captured by a camera of the user device that transmitted the image cropping to the resident device. In some embodiments, the image from which the image cropping is generated may be one of a plurality of images (e.g., non-cropped images) that are managed as part of a library of images (e.g., a photo library) comprising contacts associated with the user device. In some embodiments, the plurality of images are stored on a local memory repository of the user device. In some embodiments, the user device is a mobile device (e.g., a mobile phone). In some embodiments, the image is one of a subset of the plurality of images. The subset may be selected based at least in part on an information gain associated with the portion of the face of the first person. The information gain may be used to perform a facial recognition of the face of the first person. In some embodiments, the first image may be included within the subset (e.g., a set of reference images) based at least in part on a determination (e.g., by the transmitting user device and/or the resident device) that the portion of the face of the first person shown in the first image is not covered by a face covering. In some embodiments, the set of reference images may respectively show non-covered portions of the face of the first person. In some embodiments, the set of references may include images showing partially covered portions of faces, for example, if the resident device determines that an acceptable marginal increase in information gain is achieved by including the image in the reference set. In some embodiments, the image cropping is one of a plurality of image croppings, whereby the plurality of image croppings are respectively generated from the subset of the plurality of images. In some embodiments, the plurality of image croppings may be included within a reference set of images (e.g., image croppings) that are stored on the resident device.
At block 604, the resident device may receive from another device that includes a camera, a second image of a portion of a face of a person (e.g., a “second person,” whose identity is not yet determined). In some embodiments, the other device may correspond to an observation camera that may have a viewable area including a particular location associated with the resident device. For example, the observation camera may be positioned to have a viewable area of a front door porch of a home, an area within a home, an office space, etc. In some embodiments, the received image of block 602 and the received second image of block 604 may have a different level of image quality. In some embodiments, the level of image quality may be associated with at least one of: (1) a level of distortion, (2) an image resolution, (3) a lighting at the particular location, (4) an image occlusion, (5) an image contrast, (6) an image artifact, or (7) an image sharpness. In some embodiments, the second image may be one of a plurality of images (e.g., video frames) received from the second camera. In some embodiments, the second image may show a portion of a face of the second person that is (or is not) partially covered by a face covering (e.g., a face mask).
At block 606, the resident device may determine a score that corresponds to a level of similarity between a first set of characteristics associated with the face of the first person (e.g., see block 602) and a second set of characteristics associated with the face of the second person (e.g., see block 604). In some embodiments, the resident device may utilize a trained facial characteristics model to generate one or more faceprints. For example, the trained facial characteristics model may generate one or more faceprints that collectively correspond to the first set of characteristics. In one example, a faceprint may be created based on each reference image of the set of reference images described at block 602. In another example, a faceprint (e.g., a multidimensional vector) may incorporate data from other faceprints created into a single encompassing vector (or other suitable data structure). The trained facial characteristics model may also generate one or more faceprints based on the second image (and/or plurality of images) received from the second camera, whereby the one or more faceprints collectively correspond to the second set of characteristics. The resident device may then determine the score that corresponds to the level of similarity (e.g., a cosine similarity) between different faceprints (e.g., generated from the different sets of images). In some embodiments, upon generating respective faceprints for both the first image and the second image, the facial characteristics model may further determine one or more data points based in part on the faceprints. For example, as described herein, the facial characteristics model may determine a level of confidence that the face is partially covered by a face covering. In another example, the model may determine a level of confidence that the face is recognizable based in part on a face quality metric. These data points may be used along with the underlying faceprint vectors to determine the score. The data points may also be used to determine what data to include in a subsequent notification, described further below. In some embodiments, as described in reference to
At block 608, the resident device may determine whether the first person is the second person (e.g., whether the first image showing the first person and the second image showing the second person correspond to the same person) based at least in part on the score. In some embodiments, the resident device may first determine whether the face of the second person is recognizable or not, for example, based on analyzing the second set of characteristics (e.g., the faceprint of the second person) to determine a face quality metric for the face. The face quality metric may indicate a level of quality associated with the second set of characteristics. If the face quality metric matches (e.g., equals or exceeds) a threshold value, then the face may be determined to be recognizable. If the face is recognizable, the resident device may further determine if the face of the second person matches any of the contacts (e.g., including the first person) of the photo library of the user device. In this way, the resident device may determine the identity of the second person.
At block 610, the resident device may provide a notification based at least in part on the determination. As described herein, a notification may be provided using any suitable channel and/or according to any suitable settings. For example, one setting may indicate whether to provide a notification based in part on whether the second person's face is recognizable or not, and, if so, whether or not the second person is a known contact or not. In some embodiments, the setting may indicate whether to suppress transmission of a notification message to the user device, for example, to reduce the number of notifications being transmitted to the user device. In some embodiments, a notification may (or may not) include an image showing the portion of the face of the second person. For example, if the system determines a high level of confidence that the image shows the portion of the face partially covered with a face covering, the system may not include the image as part of the notification for subsequent presentation by the user device. In some embodiments, the image may be included, but the user device may not enable a user to tag the image for subsequent inclusion in a reference set of images of the person (e.g., a contact). In some embodiments, the user may be able to tag the image, but the system may still prevent the image from being included in the set of reference images based in part on showing a partially covered face. It should be understood that any suitable data may be included within a notification for presentation and/or subsequent tagging.
At block 702, the process 700 includes receiving a first plurality of training images (e.g., image croppings) respectively comprising a portion of a face of a person. In some embodiments, the portion of the face of the person respectively captured by each of the first plurality of training images is not partially covered by a face covering. In some embodiments, the person may be one of a plurality of people, whereby each person of the plurality of people is associated with a set of training images (e.g., collectively forming a corpus of training samples). Each image may show a portion of the person's face (e.g., side view, straight-on view) for the respective plurality of training images. In some embodiments, the first plurality of training images may include images of varying levels of quality. For example, a portion of the plurality of training images may be captured by a first camera that may be a component of (and/or otherwise connected to) a user device (e.g., a mobile phone camera). Another portion of the plurality of training images may be captured by a second camera (e.g., an observation camera). In some embodiments, the first camera may capture images having a first level of quality (or similar level of quality), for example, having a common resolution, good lighting conditions, minimal noise, etc., while the second camera may capture images with a second (e.g., lower) level of quality. In some embodiments, the second level of quality may not be different from the first level of quality. In some embodiments, each person of the plurality of people may have a corresponding plurality of training images captured by one or more cameras (e.g., forming another corpus of training samples). In some embodiments, each training image may have a corresponding label that identifies an associated person's face.
At block 704, the process 700 includes generating a second plurality of training images from a portion of the first plurality of training images. Images of the second plurality of training images may, respectively, comprise a portion of the face of the person, whereby the portion is partially covered by a simulated face covering. For example, as described herein, a portion (e.g., a subset, or all) of the first plurality of training images (e.g., received at block 702) may be selected, whereby a simulated face covering is generated for each image. For example, a trained model may determine facial landmarks for a portion of a non-covered face shown in an image. The trained model may then identify a bounding polygon that corresponds to a shape of a simulated face covering (e.g., a face mask) based on the facial landmark locations. The trained model may then render a simulated face mask according to the shape identified by the bounding polygon. In some embodiments, the system may render a simulated face mask for each of the original training images that were selected from the first plurality of training images. It should be understood that, in some embodiments, additional training samples may also be added to the first or second plurality of training images. For example, images showing a real face mask that partially covers a portion of the person may be included in the second plurality of training images, and/or used for subsequent testing a trained model. Similar to block 702, in some embodiments, each image may have a corresponding label that identifies an associated person's face.
At block 706, the process 700 includes determining a faceprint for the person based at least in part on a cross-recognition training algorithm. In some embodiments, the cross-recognition training algorithm associates characteristics of the face of the person determined from the first plurality of training images with characteristics of the face of the person determined from the second plurality of training images. In some embodiments, the cross-recognition training algorithm may operate similar to as described in reference to
Illustrative techniques for providing a notification based on determining the presence of a person at a location are described above. Some or all of these techniques may, but need not, be implemented at least partially by architectures such as those shown at least in
The various embodiments further can be implemented in a wide variety of operating environments, which in some cases can include one or more user computers, computing devices or processing devices that can be used to operate any of a number of applications. User or client devices can include any of a number of general purpose personal computers, such as desktop or laptop computers running a standard operating system, as well as cellular, wireless and handheld devices running mobile software and capable of supporting a number of networking and messaging protocols. Such a system also can include a number of workstations running any of a variety of commercially-available operating systems and other known applications for purposes such as development and database management. These devices also can include other electronic devices, such as dummy terminals, thin-clients, gaming systems and other devices capable of communicating via a network.
Most embodiments utilize at least one network that would be familiar to those skilled in the art for supporting communications using any of a variety of commercially-available protocols, such as TCP/IP, OSI, FTP, UPnP, NFS, CIFS, and AppleTalk. The network can be, for example, a local area network, a wide-area network, a virtual private network, the Internet, an intranet, an extranet, a public switched telephone network, an infrared network, a wireless network, and any combination thereof.
In embodiments utilizing a network server, the network server can run any of a variety of server or mid-tier applications, including HTTP servers, FTP servers, CGI servers, data servers, Java servers, and business application servers. The server(s) also may be capable of executing programs or scripts in response requests from user devices, such as by executing one or more applications that may be implemented as one or more scripts or programs written in any programming language, such as Java®, C, C # or C++, or any scripting language, such as Perl, Python or TCL, as well as combinations thereof. The server(s) may also include database servers, including without limitation those commercially available from Oracle®, Microsoft®, Sybase®, and IBM®.
The environment can include a variety of data stores and other memory and storage media as discussed above. These can reside in a variety of locations, such as on a storage medium local to (and/or resident in) one or more of the computers or remote from any or all of the computers across the network. In a particular set of embodiments, the information may reside in a storage-area network (SAN) familiar to those skilled in the art. Similarly, any necessary files for performing the functions attributed to the computers, servers or other network devices may be stored locally and/or remotely, as appropriate. Where a system includes computerized devices, each such device can include hardware elements that may be electrically coupled via a bus, the elements including, for example, at least one central processing unit (CPU), at least one input device (e.g., a mouse, keyboard, controller, touch screen or keypad), and at least one output device (e.g., a display device, printer or speaker). Such a system may also include one or more storage devices, such as disk drives, optical storage devices, and solid-state storage devices such as RAM or ROM, as well as removable media devices, memory cards, flash cards, etc.
Such devices also can include a computer-readable storage media reader, a communications device (e.g., a modem, a network card (wireless or wired), an infrared communication device, etc.), and working memory as described above. The computer-readable storage media reader can be connected with, or configured to receive, a non-transitory computer-readable storage medium, representing remote, local, fixed, and/or removable storage devices as well as storage media for temporarily and/or more permanently containing, storing, transmitting, and retrieving computer-readable information. The system and various devices also typically will include a number of software applications, modules, services or other elements located within at least one working memory device, including an operating system and application programs, such as a client application or browser. It should be appreciated that alternate embodiments may have numerous variations from that described above. For example, customized hardware might also be used and/or particular elements might be implemented in hardware, software (including portable software, such as applets) or both. Further, connection to other computing devices such as network input/output devices may be employed.
Non-transitory storage media and computer-readable storage media for containing code, or portions of code, can include any appropriate media known or used in the art such as, but not limited to, volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data, including RAM, ROM, Electrically Erasable Programmable Read-Only Memory (EEPROM), flash memory or other memory technology, CD-ROM, DVD or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other medium that can be used to store the desired information and that can be accessed by the a system device. Based at least in part on the disclosure and teachings provided herein, a person of ordinary skill in the art will appreciate other ways and/or methods to implement the various embodiments. However, computer-readable storage media does not include transitory media such as carrier waves or the like.
The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense. It will, however, be evident that various modifications and changes may be made thereunto without departing from the broader spirit and scope of the disclosure as set forth in the claims.
Other variations are within the spirit of the present disclosure. Thus, while the disclosed techniques are susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in the drawings and have been described above in detail. It should be understood, however, that there is no intention to limit the disclosure to the specific form or forms disclosed, but on the contrary, the intention is to cover all modifications, alternative constructions and equivalents falling within the spirit and scope of the disclosure, as defined in the appended claims.
The use of the terms “a,” “an,” and “the,” and similar referents in the context of describing the disclosed embodiments (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. The term “connected” is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. The phrase “based at least in part on” should be understood to be open-ended, and not limiting in any way, and is intended to be interpreted or otherwise read as “based at least in part on,” where appropriate. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments of the disclosure and does not pose a limitation on the scope of the disclosure unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the disclosure.
Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is otherwise understood within the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present. Additionally, conjunctive language such as the phrase “at least one of X, Y, and Z,” unless specifically stated otherwise, should also be understood to mean X, Y, Z, or any combination thereof, including “X, Y, and/or Z.”
Preferred embodiments of this disclosure are described herein, including the best mode known to the inventors for carrying out the disclosure. Variations of those preferred embodiments may become apparent to those of ordinary skill in the art upon reading the foregoing description. The inventors expect skilled artisans to employ such variations as appropriate, and the inventors intend for the disclosure to be practiced otherwise than as specifically described herein. Accordingly, this disclosure includes all modifications and equivalents of the subject matter recited in the claims appended hereto as permitted by applicable law. Moreover, any combination of the above-described elements in all possible variations thereof is encompassed by the disclosure unless otherwise indicated herein or otherwise clearly contradicted by context.
All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
As described above, one aspect of the present technology is the gathering and use of data (e.g., images of people) to perform facial recognition. The present disclosure contemplates that in some instances, this gathered data may include personally identifiable information (PII) data that uniquely identifies or can be used to contact or locate a specific person. Such personal information data can include facial characteristics, demographic data, location-based data (e.g., GPS coordinates), telephone numbers, email addresses, Twitter ID's, home addresses, or any other identifying or personal information.
The present disclosure recognizes that the use of such personal information data, in the present technology, can be used to the benefit of users. For example, the personal information data can be used to identify a person as being a contact (or not known contact) of a user of a user device.
The present disclosure contemplates that the entities responsible for the collection, analysis, disclosure, transfer, storage, or other use of such personal information data will comply with well-established privacy policies and/or privacy practices. In particular, such entities should implement and consistently use privacy policies and practices that are generally recognized as meeting or exceeding industry or governmental requirements for maintaining personal information data private and secure. Such policies should be easily accessible by users, and should be updated as the collection and/or use of data changes. Personal information from users should be collected for legitimate and reasonable uses of the entity and not shared or sold outside of those legitimate uses. Further, such collection/sharing should occur after receiving the informed consent of the users. Additionally, such entities should consider taking any needed steps for safeguarding and securing access to such personal information data and ensuring that others with access to the personal information data adhere to their privacy policies and procedures. Further, such entities can subject themselves to evaluation by third parties to certify their adherence to widely accepted privacy policies and practices. In addition, policies and practices should be adapted for the particular types of personal information data being collected and/or accessed and adapted to applicable laws and standards, including jurisdiction-specific considerations. For instance, in the US, collection of or access to certain health data may be governed by federal and/or state laws, such as the Health Insurance Portability and Accountability Act (HIPAA); whereas health data in other countries may be subject to other regulations and policies and should be handled accordingly. Hence different privacy practices should be maintained for different personal data types in each country.
Despite the foregoing, the present disclosure also contemplates embodiments in which users selectively block the use of, or access to, personal information data. That is, the present disclosure contemplates that hardware and/or software elements can be provided to prevent or block access to such personal information data. For example, in the case of services related to performing facial recognition, the present technology can be configured to allow users to select to “opt in” or “opt out” of participation in the collection of personal information data during registration for services or anytime thereafter. In addition to providing “opt in” and “opt out” options, the present disclosure contemplates providing notifications relating to the access or use of personal information. For instance, a user may be notified upon downloading an app that their personal information data will be accessed and then reminded again just before personal information data is accessed by the app.
Moreover, it is the intent of the present disclosure that personal information data should be managed and handled in a way to minimize risks of unintentional or unauthorized access or use. Risk can be minimized by limiting the collection of data and deleting data once it is no longer needed. In addition, and when applicable, including in certain health related applications, data de-identification can be used to protect a user's privacy. De-identification may be facilitated, when appropriate, by removing specific identifiers (e.g., date of birth, etc.), controlling the amount or specificity of data stored (e.g., collecting location data a city level rather than at an address level), controlling how data is stored (e.g., aggregating data across users), and/or other methods.
Therefore, although the present disclosure broadly covers use of personal information data to implement one or more various disclosed embodiments, the present disclosure also contemplates that the various embodiments can also be implemented without the need for accessing such personal information data. That is, the various embodiments of the present technology are not rendered inoperable due to the lack of all or a portion of such personal information data.
This application claims priority to U.S. Provisional Application Ser. No. 63/083,360, filed Sep. 25, 2020 entitled, “IDENTIFYING PARTIALLY COVERED OBJECTS UTILIZING SIMULATED COVERINGS,” which is hereby incorporated by reference in its entirety for all purposes. This application is related to U.S. Provisional Application No. 63/034,114, filed Jun. 3, 2020, entitled “IDENTIFYING OBJECTS WITHIN IMAGES FROM DIFFERENT SOURCES.” This application is also related to U.S. Provisional Application No. 63/034,262, filed Jun. 3, 2020, entitled “ACTIVITY ZONE FOR CAMERA VIDEO.” This application is also related to U.S. Provisional Application No. 63/034,110, filed Jun. 3, 2020, entitled “IDENTIFYING REACHABILITY OF NETWORK-CONNECTED DEVICES.” The full disclosures of which are incorporated by reference herein in their entirety for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 63083360 | Sep 2020 | US |
