Detection, counting and identification of occupants in vehicles

Information

  • Patent Grant
  • 11538257
  • Patent Number
    11,538,257
  • Date Filed
    Monday, October 8, 2018
    6 years ago
  • Date Issued
    Tuesday, December 27, 2022
    2 years ago
Abstract
A method of detecting occupants in a vehicle includes detecting an oncoming vehicle and acquiring a plurality of images of occupants in the vehicle in response to detection of the vehicle. The method includes performing automated facial detection on the plurality of images and adding a facial image for each face detected to a gallery of facial images for the occupants of the vehicle. The method includes performing automated facial recognition on the gallery of facial images to group the facial images into groups based on which occupant is in the respective facial images, and counting the final group of unique facial images to determine how many occupants are in the vehicle.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention

The present disclosure relates to facial detection and recognition, and more particularly to facial detection and recognition for occupants in vehicles.


2. Description of Related Art

At security check points, border crossings, high occupancy vehicle (HOV) lanes, and the like it, is desirable to know how many occupants are in each vehicle that passes. At a traditional checkpoint an officer can count occupants that are visible in a vehicle. In security applications, it can be desirable to know who the occupants of a vehicle are. An officer can verify this by inspection of identification documents such as a photo ID for each occupant of the vehicle. However, these techniques require each vehicle to stop for inspection before passing through.


The conventional techniques have been considered satisfactory for their intended purpose. However, there is an ever present need for improved systems and methods for detecting, counting, and identifying occupants in vehicles. This disclosure provides a solution for this need.


SUMMARY OF THE INVENTION

A system for detecting occupants in a vehicle includes a controller and a plurality of camera systems external to the vehicle in a vehicle approach area, wherein each camera system is operatively connected to the controller. A trigger in the vehicle approach area is operatively connected to the controller to detect an approaching vehicle and control the camera systems to acquire images of the approaching vehicle. The controller includes machine readable instructions configured to cause the controller to perform any method as disclosed herein.


Each camera system can include an imaging sensor, a pulsed illumination device, and a processor operatively connecting the imaging sensor to the pulsed illumination source for synchronizing illumination a pulse from the pulsed illumination device with exposure of the imaging sensor. Each camera system can include a lens optically coupled to the imaging sensor, an optical bandpass filter operatively connected to filter light passing through the lens, and a linear polarization filter operatively connected to filter light passing through the lens.


A method of detecting occupants in a vehicle includes detecting an oncoming vehicle and acquiring a plurality of images of occupants in the vehicle in response to detection of the vehicle. The method includes performing automated facial detection on the plurality of images and adding a facial image for each face detected to a gallery of facial images for the occupants of the vehicle. The method includes performing automated facial recognition on the gallery of facial images to group the facial images into groups based on which occupant is in the respective facial images, and counting the groups to determine how many occupants are in the vehicle.


The method can include selecting a representative image from each group, and outputting a set of cropped selected images, one uniquely cropped selected image for each of the occupants. It is contemplated that no duplicate images of a given occupant need be stored or displayed. Selecting the representative image from each group can include selecting images based on corresponding confidence scores from the automated facial detection. Selecting the representative image from each group can include selecting images based on which image in the group has least facial offset angle from line of sight of an imaging sensor which acquired the respective image. The method can include running the selective images through a database to check for matches between the occupants and known individuals in the database. The method can include initiating a response upon finding a match in the database, wherein the response include at least one of outputting an alert on a visual display, sounding an audible alarm, closing a physical barrier, transmitting a citation, mailing a citation, and/or dispatching an officer. It is also contemplated that the method can include initiating a response upon determining an improper number of occupants in the vehicle, wherein the response includes at least one of outputting an alert on a visual display, sounding an audible alarm, closing a physical barrier, transmitting a citation, mailing a citation, and/or dispatching an officer.


Each image can be acquired from a different sensor viewing the vehicle from a different respective angle. The method can include illuminating the vehicle with a respective pulse of illumination for each image acquired, wherein each pulse of illumination is performed at a different time to reduce shadows cast onto the occupants while acquiring the plurality of images.


One of the sensors can be a primary sensor that acquires a primary image of occupants in the vehicle, wherein faces detected in primary image serve as references in the gallery for facial recognition for subsequent ones of the images of occupants in the vehicle. The method can include adding a new face to the gallery each time a detected face in a subsequent one of the images of occupants in the vehicle does not match with a face already in the gallery. The method can include iteratively comparing faces detected in subsequent ones of the images of occupants in the vehicle and adding each face detected to the gallery that is not already in the gallery until there is an image in the gallery of each face detected by performing automated facial detection.


These and other features of the systems and methods of the subject disclosure will become more readily apparent to those skilled in the art from the following detailed description of the preferred embodiments taken in conjunction with the drawings.





BRIEF DESCRIPTION OF THE DRAWINGS

So that those skilled in the art to which the subject disclosure appertains will readily understand how to make and use the devices and methods of the subject disclosure without undue experimentation, preferred embodiments thereof will be described in detail herein below with reference to certain figures, wherein:



FIG. 1 is a schematic side elevation view of an exemplary embodiment of a system constructed in accordance with the present disclosure, showing multiple camera systems with an approaching vehicle;



FIG. 2 is a schematic plan view of the system of FIG. 1, showing the positions of the camera systems;



FIG. 3 is a schematic view of one of the camera systems of FIGS. 1 and 2, showing the imaging sensor; and



FIG. 4 is a schematic view of a method in accordance with the subject disclosure, showing a process using images acquired by the system of FIG. 1.





DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Reference will now be made to the drawings wherein like reference numerals identify similar structural features or aspects of the subject disclosure. For purposes of explanation and illustration, and not limitation, a partial view of an exemplary embodiment of a system in accordance with the disclosure is shown in FIG. 1 and is designated generally by reference character 100. Other embodiments of systems in accordance with the disclosure, or aspects thereof, are provided in FIGS. 2-4, as will be described. The systems and methods described herein can be used for automated counting and identification of occupants in vehicles.


The system 100 for detecting occupants in a vehicle 102 includes a controller 104 and a plurality of camera systems 106, 108, and 110 that are external to the vehicle 105 in the vehicle approach area 112. Each camera system 106, 108, and 110 is operatively connected to the controller 104. A trigger 114 in the vehicle approach area 112 is operatively connected to the controller 104 to detect an approaching vehicle 105 and to control the camera systems 106, 108, and 110 to acquire images of the approaching vehicle 105. The controller 104 includes machine readable instructions configured to cause the controller 104 to perform any method as disclosed herein. As shown in FIGS. 1 and 2, each camera system 106, 108, and 110 is in a different location for acquiring images with sensors viewing the vehicle from different respective angles.


With reference now to FIG. 3, camera system 106 includes an imaging sensor 116, a pulsed illumination device 118, and a processor 120 operatively connecting the imaging sensor 116 to the pulsed illumination source 118 for synchronizing an illumination pulse from the pulsed illumination device 118 with exposure of the imaging sensor 116. The illumination device 118 can be located on camera as in camera system 106 shown in FIG. 3, or can be located off-camera as in camera systems 108 and 110 shown in FIGS. 1-2. The camera system 106 include a lens 122 optically coupled to the imaging sensor 116, an optical bandpass filter 124 operatively connected to filter light passing through the lens 122 to the imaging sensor 116. The camera system 106 also includes a linear polarization filter 126 operatively connected to filter light passing through the lens 122 to the sensor 116, e.g., to reduce glare from glass windshields and windows of the vehicle 105. Imaging sensors 108 and 110 can include the same components as camera system 106.


With reference now to FIG. 4, a method of detecting occupants in a vehicle includes detecting an oncoming vehicle, e.g., detecting oncoming vehicle 105 using trigger 114 as shown in FIG. 1. When trigger 114 detects an oncoming vehicle 105, it signals the controller 104. Controller 104 then commands the camera systems 106, 108, and 110 to acquire a plurality of images of occupants in the vehicle 105. Each camera system 106, 108, and 110 can acquire a respective image 128, 130, and 132, forming a set 200 of acquired images as shown in FIG. 4. Controller 104 can illuminate the vehicle 105 with a respective pulse of illumination from each respective illumination device 118 for each image acquired, wherein each pulse of illumination is performed at a different time to reduce shadows cast onto the occupants while acquiring the plurality of images.


The method includes having controller 104 perform automated facial detection on the plurality of images 128, 130, and 132, and to add a facial image for each face detected to a gallery 202 of facial images for the occupants of the vehicle 105. For the image 128, three faces are detected and four faces are detected from each of images 130 and 132. Controller 104 performs automated facial recognition on the facial images of gallery 202 to group the facial images into groups 134, 136, 138, and 140 based on which occupant is in the respective facial images, as indicated by facial recognition groupings 204 in FIG. 4. While multiple images are shown in FIG. 4 in each of the groups 134, 136, 138, and 140, it should be understood that the groups 134, 136, 138, and 140 need not ever actually contain multiple images in each group. For example during facial recognition, each time a new image of a given individual is identified, controller 104 can decide whether the new image is better than the previous best image of the individual (based on facial detection confidence scores, facial offset angle, or the like, as described below), and if so the new image replaces the previous image in the respective group. In this way each group 134, 136, 138, and 140 only ever includes one image.


Facial detection and facial recognition need not necessarily be performed one after another, but instead can be performed together on the fly. One of the sensors 120 can be a primary sensor, e.g., the sensor 120 of camera system 106, that acquires a primary image, e.g., image 128, of occupants in the vehicle 105. The faces detected in primary image 128 can serve as references in the gallery 202 for facial recognition for subsequent ones of the images 130 and 132 of occupants in the vehicle. The controller 104 can add a new face to the gallery 202 each time a detected face in a subsequent one of the images 130 and 132 does not match with a face already in the gallery 202. The controller 104 can iteratively compare faces detected in subsequent ones of the images 128, 130, and 132 and add each face detected to the gallery 202 that is not already in the gallery 202 until there is an image in the gallery 202 of each face detected by performing automated facial detection.


Whenever a face is detected for which there is already an image in the gallery 202, the best image of the face can be retained in the image gallery 202. Controller 104 selects a representative image 142, 144, 146, and 148 from each group 134, 136, 138, and 145 and can output a set 206 of cropped selected images, one uniquely cropped selected image for each of the occupants. Set 206 includes no duplicate images, i.e. no more than one image is in set 206 for a given occupant, so duplicate images of a given occupant need be stored or displayed. The controller 104 can select the representative image 142, 144, 146, and 148 from each group 134, 136, 138, and 140 by selecting images based on corresponding confidence scores from the automated facial detection. It is also contemplated that controller 104 can selecting the representative image 142, 144, 146, and 148 from each group 134, 136, 138, and 140 by selecting images based on which image in the group has least facial offset angle from line of sight of the imaging sensor 120 which acquired the respective image. This selection process can be run on the fly with facial detection and facial recognition to winnow the gallery 202 down to the set 206.


The controller 104 can determine how many occupants are in the vehicle 105 by counting the groups 134, 136, 138, and 140. In this example, there are four groups 134, 136, 138, and 140 indicating there are four occupants in the vehicle 105. If groups 134, 136, 138, and 140 are conflated down to the set 206 on the fly as described above, then the groups 134, 136, 138, and 140 can be counted indirectly by simply counting the final cropped images in set 206 to determine how many occupants are in the vehicle 105.


The controller 104 can output the number of occupants in the vehicle 105, and can provide other output actions as needed. For example, controller 104 can initiate a response, e.g., via the output device 150, upon determining an improper number of occupants in the vehicle. For example, if controller 104 determines there are not enough occupants in a vehicle in an HOV lane, controller 150 can use the output device 150 to output an alert on a visual display, sound an audible alarm, close a physical barrier, transmit a citation, mail a citation, update a database, and/or dispatch an officer.


It is also contemplated that with the set of images 206, controller 104 can run the final cropped facial images through a facial recognition database, either locally or remotely, to check for matches between the occupants and known individuals in the database. If a match is found, e.g., one of the occupants in the vehicle 105 is on a watch list, the controller 104 can initiate an output response, e.g., using output device 150, such as outputting an alert on a visual display, sounding an audible alarm, closing a physical barrier, transmitting a citation, mailing a citation, updating a database, and/or dispatching an officer.


While shown and described herein in an exemplary context where there are n=3 camera systems and m=4 occupants in the vehicle 105, those skilled in the art will readily appreciate that any suitable number n of camera systems can be used, and any suitable number m of occupants in a vehicle can be counted/identified without departing from the scope of this disclosure.


The methods and systems of the present disclosure, as described above and shown in the drawings, provide for counting and identifying occupants in vehicles with superior properties including reliable, automated detection and identification of all occupants in a moving vehicle. While the apparatus and methods of the subject disclosure have been shown and described with reference to preferred embodiments, those skilled in the art will readily appreciate that changes and/or modifications may be made thereto without departing from the scope of the subject disclosure.

Claims
  • 1. A method of detecting occupants in a vehicle using a system comprising a first camera positioned on a driver's side of the vehicle and a second camera positioned on a passenger's side of the vehicle, the method comprising: detecting an oncoming vehicle;using the first camera and the second camera, acquiring a plurality of images of occupants in the vehicle in response to detection of the vehicle, wherein the first camera acquires a first portion of the plurality of images from a first angle, wherein the second camera acquires a second portion of the plurality of images from a second angle, and wherein the first angle is different from the second angle;performing automated facial detection on the plurality of images;based on the automated facial detection, generating a gallery of facial images from the plurality of images, wherein the gallery of facial images comprises a first facial image of at least one of the occupants taken from the first angle and a second facial image of said at least one of the occupants taken from the second angle, thereby resulting in said gallery of facial images comprising multiple images of said at least one of the occupants;performing, after forming the gallery of facial images, automated facial recognition on the gallery of facial images to form facial groupings, wherein each of the facial groupings comprise facial images of only one of the occupants and wherein at least one of said facial groupings comprises said multiple images of the at least one of the occupants and no facial images of other ones of said occupants;generating a confidence score for each of the images in each of the facial groupings based on the automated facial detection performed on each of the facial groupings;selecting a representative image from each of the facial groupings based on the corresponding confidence score;outputting the representative image from each of the facial groupings to create a set of unique images, each of which is representative of only one of the occupants; andcounting the groups to determine how many occupants are in the vehicle.
  • 2. The method as recited in claim 1, wherein no multiple images of a given occupant are stored or displayed.
  • 3. The method as recited in claim 1, wherein selecting the representative image from each of the facial groupings comprises: for each image in each of the facial groupings, determining a facial offset angle from a line of sight of an imaging sensor that acquired the image; andselecting the representative image based on which image in the facial grouping has a smallest facial offset angle.
  • 4. The method as recited in claim 1, further comprising: comparing each of the unique images to images of known individuals in a database and determining if any of the unique images match at least one of said images of known individuals.
  • 5. The method as recited in claim 4, further comprising initiating a response upon finding a match in the database, wherein the response includes at least one of outputting an alert on a visual display, sounding an audible alarm, closing a physical barrier, transmitting a citation, mailing a citation, or dispatching an officer.
  • 6. The method as recited in claim 1, further comprising initiating a response upon determining an improper number of occupants in the vehicle, wherein the response includes at least one of outputting an alert on a visual display, sounding an audible alarm, closing a physical barrier, transmitting a citation, mailing a citation, or dispatching an officer.
  • 7. The method as recited in claim 1, further comprising illuminating the vehicle with a respective pulse of illumination for each image acquired, wherein each pulse of illumination is performed at a different time to reduce shadows cast onto the occupants while acquiring the plurality of images.
  • 8. The method as recited in claim 1, further comprising capturing a primary image of the occupants in the vehicle, applying facial detection to the primary image to generate reference images and using said reference images in the automated facial recognition to form the facial groupings.
  • 9. The method as recited in claim 8, further comprising adding a new face to the gallery of facial images each time a detected face does not match with a face already in the gallery of facial images.
  • 10. The method as recited in claim 8, further comprising adding each face detected to the gallery of facial images that is not already in the gallery until there is an image in the gallery of facial images of each face detected by said automated facial detection process.
  • 11. A system for detecting occupants in a vehicle using a plurality of images, comprising: a controller;a first camera positioned on a driver's side of the vehicle and a second camera positioned on a passenger's side of the vehicle, wherein the first camera and the second camera are operatively coupled to the controller, and wherein each of the first camera and the second camera comprises an imaging sensor, a pulsed illumination device, and a processor operatively coupling the imaging sensor to the pulsed illumination source for synchronizing pulses of illumination with exposure of the imaging sensor; anda trigger operatively connected to the controller to detect the vehicle and control the first camera and the second camera to acquire images of the vehicle, wherein the first camera acquires a first portion of the plurality of images from a first angle, wherein the second camera acquires a second portion of the plurality of images from a second angle, and wherein the first angle is different from the second angle; andmachine readable instructions stored in a non-transient storage medium and configured to be executed by the controller, wherein executing the machine readable instructions causes the controller to:detect an oncoming vehicle based upon the trigger;acquire the first portion of the plurality of images and the second portion of the plurality of images;perform automated facial detection on the plurality of images;based on the automated facial detection, generate a gallery of facial images from the plurality images, wherein the gallery of facial images comprises a first facial image of at least one of the occupants taken from the first angle and a second facial image of said at least one of the occupants taken from the second angle, thereby resulting in said gallery of facial images comprising multiple images of said at least one of the occupants;perform, after formation of the gallery of facial images, automated facial recognition on the gallery of facial images to form facial groupings, wherein each of the facial groupings comprise facial images of only one of the occupants and wherein at least one of said facial groupings comprises said multiple images of the at least one of the occupants and no facial images of other ones of said occupants;generate a confidence score for each of the images in the facial groupings based on the automated facial detection performed on each of the facial groupings;select a representative image from each of the facial groupings based on the corresponding confidence scores;output the representative image from each of the facial groupings to create a set of unique images, each of which is representative of only one of the occupants; andcount the groups to determine how many occupants are in the vehicle.
  • 12. The system as recited in claim 11, wherein the machine readable instructions comprise instructions that, when executed, cause the controller to illuminate the vehicle with a pulse of illumination from the pulsed illumination device for each of the plurality of images, wherein each pulse of illumination is performed at a different time to reduce shadows cast onto the occupants while acquiring the plurality of images.
  • 13. The system as recited in claim 11, wherein each of the first camera and the second camera comprises a lens optically coupled to the imaging sensor, and at least one of an optical bandpass filter operatively configured to filter light passing through the lens or a linear polarization filter operatively configured to filter light passing through the lens.
  • 14. The system as recited in claim 1, wherein the machine readable instructions comprise instructions that, when executed, cause the controller to determine a facial offset angle from a line of sight of an imaging sensor that acquired the image for each image in each of the facial groupings and select the representative image based on which image in the facial grouping has a smallest facial offset angle.
  • 15. A method of detecting occupants in a vehicle using a system comprising a first camera positioned on a driver's side of the vehicle and a second camera positioned on a passenger's side of the vehicle, The method comprising: detecting an oncoming vehicle;using the first camera and the second camera, acquiring a plurality of images of occupants in the vehicle in response to detection of the vehicle, wherein the first camera acquires a first portion of the plurality of images from a first angle, wherein the second camera acquires a second portion of the plurality of images from a second angle, and wherein the first angle is different from the second angle;performing automated facial detection on the plurality of images;based on the automated facial detection, generating a gallery of facial images from the plurality of images, wherein the gallery of facial images comprises a first facial image of at least one of the occupants taken from the first angle and a second facial image of said at least one of the occupants taken from the second angle, thereby resulting in said gallery of facial images comprising multiple images of said at least one of the occupants;performing, after forming the gallery of facial images, automated facial recognition on the gallery of facial images to form facial groupings, wherein each of the facial groupings comprise facial images of only one of the occupants and wherein at least one of said facial groupings comprises said multiple images of the at least one of the occupants and no facial images of other ones of said occupants;determining a facial offset angle from a line of sight of an imaging sensor that acquired the image for each image in each of the facial groupings;selecting a representative image from each of the facial groupings based on which image in the facial grouping has a smallest offset angle;outputting the representative image from each of the facial groupings to create a set of unique images, each of which is representative of only one of the occupants; andcounting the groupings to determine how many occupants are in the vehicle.
  • 16. The method as recited in claim 15, wherein no multiple images of a given occupant are stored or displayed.
  • 17. The method as recited in claim 15, further comprising: comparing each of the unique images to images of known individuals in a database and determining if any of the unique images match at least one of said images of known individuals.
  • 18. The method as recited in claim 15, further comprising initiating a response upon finding a match in the database, wherein the response includes at least one of outputting an alert on a visual display, sounding an audible alarm, closing a physical barrier, transmitting a citation, mailing a citation, or dispatching an officer.
  • 19. The method as recited in claim 15, further comprising initiating a response upon determining an improper number of occupants in the vehicle, wherein the response includes at least one of outputting an alert on a visual display, sounding an audible alarm, closing a physical barrier, transmitting a citation, mailing a citation, or dispatching an officer.
  • 20. The method as recited in claim 15, further comprising illuminating the vehicle with a respective pulse of illumination for each image acquired, wherein each pulse of illumination is performed at a different time to reduce shadows cast onto the occupants while acquiring the plurality of images.
  • 21. The method as recited in claim 15, further comprising capturing a primary image of the occupants in the vehicle, applying facial detection to the primary image to generate reference images and using said reference images in the automated facial recognition to form the facial groupings.
  • 22. The method as recited in claim 21, further comprising adding a new face to the gallery of facial images each time a detected face does not match with a face already in the gallery of facial images.
  • 23. The method as recited in claim 21, further comprising adding each face detected to the gallery of facial images that is not already in the gallery until there is an image in the gallery of facial images of each face detected by said automated facial detection process.
  • 24. A system for detecting occupants in a vehicle using a plurality of images, comprising: a controller;a first camera positioned on a driver's side of the vehicle and a second camera positioned on a passenger's side of the vehicle, wherein the first camera and second camera are operatively coupled to the controller and wherein each of the first camera and the second camera comprises an imaging sensor, a pulsed illumination device, and a processor operatively coupling the imaging sensor to the pulsed illumination source for synchronizing pulses of illumination with exposure of the imaging sensor; anda trigger operatively connected to the controller to detect the vehicle and control the first camera and the second camera to acquire images of the vehicle, wherein the first camera acquires a first portion of the plurality of images from a first angle, wherein the second camera acquires a second portion of the plurality of images from a second angle, and wherein the first angle is different from the second angle; andmachine readable instructions stored in a non-transient storage medium and configured to be executed by the controller, wherein executing the machine readable instructions causes the controller to:detect an oncoming vehicle based upon the trigger;acquire the first portion of the plurality of images and the second portion of the plurality of images;perform automated facial detection on the plurality of images;based on the automated facial detection, generate a gallery of facial images from the plurality images, wherein the gallery of facial images comprises a first facial image of at least one of the occupants taken from the first angle and a second facial image of said at least one of the occupants taken from the second angle, thereby resulting in said gallery of facial images comprising multiple images of said at least one of the occupants;perform, after formation of the gallery of facial images, automated facial recognition on the gallery of facial images to form facial groupings, wherein each of the facial groupings comprise facial images of only one of the occupants and wherein at least one of said facial groupings comprises said multiple images of the at least one of the occupants and no facial images of other ones of said occupants;determine a facial offset angle from a line of sight of an imaging sensor that acquired the image for each image in each of the facial groupings;select a representative image from each of the facial groupings based on which image in the facial grouping has a smallest facial offset angle;output the representative image from each of the facial groupings to create a set of unique images, each of which is representative of only one of the occupants; andcount the groupings to determine how many occupants are in the vehicle.
  • 25. The system as recited in claim 24, wherein the machine readable instructions comprise instructions that, when executed, cause the controller to illuminate the vehicle with a pulse of illumination from the pulsed illumination device for each of the plurality of images, wherein each pulse of illumination is performed at a different time to reduce shadows cast onto the occupants while acquiring the plurality of images.
  • 26. The system as recited in claim 25, wherein each of the first camera and the second camera comprises a lens optically coupled to the imaging sensor, and at least one of an optical bandpass filter operatively configured to filter light passing through the lens or a linear polarization filter operatively configured to filter light passing through the lens.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of priority to U.S. Provisional Patent Application No. 62/596,497 filed Dec. 8, 2017, which is incorporated by reference herein in its entirety.

US Referenced Citations (172)
Number Name Date Kind
4288819 Williams Sep 1981 A
5091924 Bermbach Feb 1992 A
5119236 Fong Jun 1992 A
5283643 Fujimoto Feb 1994 A
5343390 Doi Aug 1994 A
5361840 Matthews Nov 1994 A
5449864 Beatty Sep 1995 A
6313946 Petitto Nov 2001 B1
6400835 Lemelson Jun 2002 B1
6459764 Chalmers Oct 2002 B1
6611200 Pressnall Aug 2003 B2
6650765 Alves Nov 2003 B1
6718049 Pavlidis Apr 2004 B2
6856344 Frantz Feb 2005 B2
6958676 Morgan Oct 2005 B1
6972693 Brown Dec 2005 B2
7076088 Pavlidis Jul 2006 B2
7092106 Cox Aug 2006 B2
7102665 Chandler Sep 2006 B1
7132653 Faubion Nov 2006 B2
7305108 Waehner Dec 2007 B2
7349007 Millar Mar 2008 B2
7439847 Pederson Oct 2008 B2
7469060 Bazakos Dec 2008 B2
7602942 Bazakos Oct 2009 B2
7602947 Lemelson Oct 2009 B1
7642899 Alvarado Jan 2010 B2
7689033 Xiao Mar 2010 B2
7786897 Alves Aug 2010 B2
7792970 Bigioi Sep 2010 B2
8005267 Chew Aug 2011 B2
8028903 Daniel Oct 2011 B1
8054182 Cutchis Nov 2011 B2
8067719 Herrera Nov 2011 B2
8155384 Chew Apr 2012 B2
8254647 Nechyba Aug 2012 B1
8305442 Millar Nov 2012 B2
8358343 Millar Jan 2013 B2
8509486 Hsieh Aug 2013 B2
8604901 Hoyos Dec 2013 B2
8830322 Nerayoff Sep 2014 B2
8861802 Bedros Oct 2014 B2
9087204 Gormley Jul 2015 B2
9105128 Robinson Aug 2015 B2
9189680 Komatsu Nov 2015 B2
9230183 Bechtel Jan 2016 B2
9256794 Braithwaite Feb 2016 B2
9292754 Shin Mar 2016 B2
9396595 Daniel Jul 2016 B1
9460598 Noone Oct 2016 B2
9471838 Miller Oct 2016 B2
9533687 Lisseman Jan 2017 B2
9552524 Artan Jan 2017 B2
9600712 Jin Mar 2017 B2
9613258 Chen Apr 2017 B2
9623878 Tan Apr 2017 B2
9667627 Gormley May 2017 B2
9791766 Ekin Oct 2017 B2
9953149 Tussy Apr 2018 B2
9953210 Rozploch Apr 2018 B1
10146797 Bataller Dec 2018 B2
10262126 Tussy Apr 2019 B2
10657360 Rozploch May 2020 B2
10674587 Sinitsyn Jun 2020 B2
10839200 Nazemi Nov 2020 B2
10867193 Hansen Dec 2020 B1
11087119 Nazemi Aug 2021 B2
11196965 Hansen Dec 2021 B2
20020092988 Didomenico Jul 2002 A1
20030174865 Vernon Sep 2003 A1
20030185340 Frantz Oct 2003 A1
20030209893 Breed Nov 2003 A1
20040070679 Pope Apr 2004 A1
20040165750 Chew Aug 2004 A1
20040199785 Pederson Oct 2004 A1
20040225651 Musgrove Nov 2004 A1
20050063566 Beek Mar 2005 A1
20050105806 Nagaoka May 2005 A1
20050110610 Bazakos May 2005 A1
20050271184 Ovadia Dec 2005 A1
20060018522 Sunzeri Jan 2006 A1
20060028556 Bunn Feb 2006 A1
20060055512 Chew Mar 2006 A1
20060102843 Bazakos May 2006 A1
20060117186 Yeo Jun 2006 A1
20060146062 Kee Jul 2006 A1
20060284982 Bigioi Dec 2006 A1
20070030350 Wagner Feb 2007 A1
20070087756 Hoffberg Apr 2007 A1
20070112699 Zhao May 2007 A1
20070122007 Austin May 2007 A1
20070133844 Waehner Jun 2007 A1
20080175438 Alves Jul 2008 A1
20080211914 Herrera Sep 2008 A1
20080285803 Madsen Nov 2008 A1
20080297611 Qiu Dec 2008 A1
20080298644 Irmatov Dec 2008 A1
20090023472 Yoo Jan 2009 A1
20090080715 Van Beek Mar 2009 A1
20090232365 Berthilsson Sep 2009 A1
20090303342 Corcoran Dec 2009 A1
20100158380 Neville Jun 2010 A1
20110182473 Wang Jul 2011 A1
20110242285 Byren Oct 2011 A1
20120069183 Aoki Mar 2012 A1
20120106806 Folta May 2012 A1
20120140079 Millar Jun 2012 A1
20120262577 Wang Oct 2012 A1
20120328197 Sanderson Dec 2012 A1
20130129159 Huijgens May 2013 A1
20130147959 Wang Jun 2013 A1
20130176285 Sato Jul 2013 A1
20130202274 Chan Aug 2013 A1
20130236068 Eshghi et al. Sep 2013 A1
20130243260 Burry Sep 2013 A1
20130251214 Chung Sep 2013 A1
20130266193 Tiwari Oct 2013 A1
20130266196 Kono Oct 2013 A1
20130279757 Kephart Oct 2013 A1
20130336538 Skaff Dec 2013 A1
20140002617 Zhang Jan 2014 A1
20140029005 Fiess Jan 2014 A1
20140044348 Chen Feb 2014 A1
20140063177 Tian Mar 2014 A1
20140132501 Choi May 2014 A1
20140132746 King May 2014 A1
20140253701 Wexler Sep 2014 A1
20140254890 Bergman Sep 2014 A1
20140285315 Wiewiora Sep 2014 A1
20140320281 Sager Oct 2014 A1
20140334684 Strimling Nov 2014 A1
20150131872 Ganong May 2015 A1
20150186711 Baldwin Jul 2015 A1
20150261994 Yamaji Sep 2015 A1
20150262024 Braithwaite Sep 2015 A1
20150278617 Oami Oct 2015 A1
20150286883 Xu Oct 2015 A1
20150294144 Konishi Oct 2015 A1
20150317535 Lenor Nov 2015 A1
20150331105 Bell Nov 2015 A1
20150347860 Meier Dec 2015 A1
20150357000 Howell Dec 2015 A1
20150363655 Artan Dec 2015 A1
20160026855 Mazumdar Jan 2016 A1
20160063235 Tussy Mar 2016 A1
20160171312 Aoki Jun 2016 A1
20160171808 Caterino Jun 2016 A1
20160178936 Yang Jun 2016 A1
20160217319 Bhanu Jul 2016 A1
20160239714 Oami Aug 2016 A1
20160253331 Roshen Sep 2016 A1
20160300410 Jones Oct 2016 A1
20160343251 Lee Nov 2016 A1
20160379043 Fazl Ersi Dec 2016 A1
20170046808 Parrish Feb 2017 A1
20170068863 Rattner Mar 2017 A1
20170076140 Waniguchi Mar 2017 A1
20170106892 Lisseman Apr 2017 A1
20180018351 Fagans Jan 2018 A1
20180082131 Li Mar 2018 A1
20180089528 Chan Mar 2018 A1
20180157922 Miyamoto Jun 2018 A1
20180181737 Tussy Jun 2018 A1
20180189551 Ranganath Jul 2018 A1
20180196587 Bialynicka-Birula Jul 2018 A1
20180225307 Kocher Aug 2018 A1
20180306598 Decia Oct 2018 A1
20180307915 Olson Oct 2018 A1
20190089934 Goulden Mar 2019 A1
20190354750 Nazemi Nov 2019 A1
20190373157 Kunihiro Dec 2019 A1
20220094880 Hansen Mar 2022 A1
Foreign Referenced Citations (33)
Number Date Country
3010922 Sep 2017 CA
102682295 Sep 2012 CN
104024827 Sep 2014 CN
105785472 Jul 2016 CN
10101341 Jul 2002 DE
102015002802 Aug 2015 DE
102014214352 Jan 2016 DE
1482329 Dec 2004 EP
2620896 Jul 2013 EP
2993619 Mar 2016 EP
2395105 Feb 2013 ES
2258321 Feb 1993 GB
2003348573 Dec 2003 JP
4366008 Nov 2009 JP
05997871 Sep 2016 JP
1020050003664 Jan 2005 KR
20090031136 Mar 2009 KR
100964025 Jun 2010 KR
100964886 Jun 2010 KR
101252671 Apr 2013 KR
101514444 Apr 2015 KR
20150137666 Dec 2015 KR
101628390 Jun 2016 KR
20190030960 Mar 2019 KR
200146668 Jun 2001 WO
2004110054 Dec 2004 WO
2012160251 Nov 2012 WO
WO-2013004864 Jan 2013 WO
2014054328 Apr 2014 WO
2014110629 Jul 2014 WO
2015120413 Aug 2015 WO
2016183408 Nov 2016 WO
2017151859 Sep 2017 WO
Non-Patent Literature Citations (17)
Entry
F. Z. Chelali, A. Djeradi and R. Djeradi, “Linear discriminant analysis for face recognition,” 2009 International Conference on Multimedia Computing and Systems, Ouarzazate, 2009, pp. 1-10, doi: 10.1109/MMCS.2009.5256630.
Shishir Bashyal and Ganesh K. Venayagamoorthy, “Recognition of facial expressions using Gabor wavelets and learning vector quantization”, Missouri University of Science and Technology, MO 65409, USA, Received Feb. 2, 2005; received in revised form Apr. 26, 2007; accepted Nov. 12, 2007.
Jin Wei, Zhang Jian-qi, Zhang Xiang, “Face recognition method based on support vector machine and particle swarm optimization”, © 2010 Elsevier Ltd. All rights reserved. doi: 10.1016/j.eswa.2010.09.108.
Pavlidis et al., “Automatic Passenger Counting in the High Occupany Vehicle (HOV) Lanes,” 19 pages, prior to Oct. 20, 2005.
P. Jonathon Phillips,“Support Vector Machines Applied to Face Recognition”, this is technical report NISTIR 6241, to appear in Advances in Neural Information, Processing Systems 11, eds. M. J. Kearns, S. A. Solla, and D. A. Cohn, MIT Press, 1999.
Huaqing Li, Shaoyu Wang, and Feihu Qi, R. Klette and J. Zuni'c (Eds.),“Automatic Face Recognition by Support Vector Machines”: IWCIA 2004, LNCS 3322, pp. 716-725, 2004. © Springer-Verlag Berlin Heidelberg 2004.
Jia Hao, Yusuke Morishita, Toshinori Hosoi, Kazuyuki Sakurai, Hitoshi Imaoka, Takao Imaizumi, and Hideki Irisawa, “Large-scale Face Recognition on Smart Devices”, 2013 Second IAPR Asian Conference on Pattern Recognition, 978-1-4799-2190-4/13, © 2013 IEEE, DOI 10.1109/ACPR.2013.189.
PCT International Search Report and Written Opinion dated Feb. 21, 2019, issued during the prosecution of PCT International Patent Application No. PCT/US2018/64444 (15 pages).
Extended European Search Report for European Patent Application No. EP18885197.6, dated Jul. 9, 2021.
Viisage Technology, Inc. “FaceFINDER 2.5”, Data Sheet, pp. 2 page; https://www.epic.org/privacy/surveillance/cptolight/1105/facefinder.pdf, 2004.
Dickson, Peter et al. “Mosaic Generation for Under Vehicle Inspection”, Applications of Computer Vision, 2002. (WACV 2002), Pascataway, NJ, Dec. 3, 2022, pp. 251-256.
International Search Report and Written Opinion for PCT/US06/06708, dated Aug. 29, 2006.
International Search Report and Written Opinion for PCT/US2019/031755, dated Sep. 5, 2019.
International Search Report and Written Opinion for PCT/US2018/064444, dated Feb. 21, 2019.
International Search Report and Written Opinion for PCT/US2020/056429, dated Feb. 9, 2021.
International Search Report and Written Opinion for PCT/US2020/041195, dated Oct. 21, 2020.
International Search Report and Written Opinion for PCT/US2022/013783, dated May 16, 2022.
Related Publications (1)
Number Date Country
20190180125 A1 Jun 2019 US
Provisional Applications (1)
Number Date Country
62596497 Dec 2017 US