The invention relates to a method for analyzing and evaluating a face, and specifically to a method for analyzing and evaluating the facial muscle status of the face.
Human's muscle (especially facial muscle) will slowly slacken and droop while people age, and parts of the users choose to use care products to maintain their muscle and skin, such as use cosmetics to cover the slackened muscle, or go to work out for slowing down the speed of muscle slackening.
General speaking, users will be sitting in front of the mirror for using the care products and/or the cosmetics, or using the care products and/or the cosmetics through the assistance of smart phones, laptops or special makeup assisting devices in order to improve the speed and quality of using the same.
However, such devices can only assist the users in using the care products/cosmetics, but it is incapable of actively analyzing user's muscle status. Therefore, users cannot be aware of whether the care products/cosmetics they have been using are effective after using it for a period of time. Besides, even if the users constantly do the exercise or go to the aesthetic medicine clinic for miro plastic surgery, users can only use bare eyes to determine their skin condition by their own, but still they cannot ensure whether the exercise or miro plastic surgery they did are really helping their skin.
According to the above problem, a novel analyzing and evaluating method should be provided in this field for effectively analyzing and evaluating user's current muscle status, so the user can easily realize whether the currently applied maintenance manners are effective.
The invention is directed to a method for analyzing and evaluating facial muscle status of a face, which can obtain multiple ideal muscle identifying points as well as multiple actual muscle identifying points from user's face after analyzing the face, so as to evaluate user's current facial muscle status.
In one of the exemplary embodiments, the method of the present invention is applied to a face image analyzing apparatus and includes following steps: capturing user's face image through an image capturing unit of the face image analyzing apparatus; analyzing the face image through an analyzing algorithm for obtaining multiple ideal muscle identifying points corresponding to the frame of user's face in the face image; identifying the face image through a fuzzy comparison algorithm and a training model for obtaining multiple actual muscle identifying points corresponding to actual muscle status of user's face; evaluating each of the actual muscle identifying points based on the multiple ideal muscle identifying points in company with a pre-stored evaluation rule; and, displaying the multiple ideal muscle identifying points, the multiple actual muscle identifying points and evaluated results on a display of the face image analyzing apparatus.
In comparison with related art, the disclosures of the present invention can analyze and evaluate user's face through comparing multiple ideal muscle identifying points with multiple actual muscle identifying points upon user's face, so as to assist the user to be aware of his/her current facial muscle status in order to ensure whether the currently applied maintenance manners are effective or not.
In cooperation with the attached drawings, the technical contents and detailed description of the present invention are described thereinafter according to multiple embodiments, being not used to limit its executing scope. Any equivalent variation and modification made according to appended claims is all covered by the claims claimed by the present invention.
The present invention discloses a method for analyzing and evaluating facial muscle status (referred to as the evaluating method hereinafter), and the evaluating method is mainly applied to a face image analyzing apparatus 1 (referred to as the analyzing apparatus 1 hereinafter) as shown in
The analyzing apparatus 1 shown in
As shown in
The analyzing apparatus 1 can capture a photo of the user (especially a photo includes the user's face) through the image capturing unit 12, and retrieve a face image of the user from the photo and displays the face image through the display 11. Also, the analyzing apparatus 1 may display guidance information (for example, directly labelling each makeup area on the face image to indicate the user which cosmetic to use, or utilizing words or pictures to provide makeup steps or suggestions to the user) on the display 11. Therefore, the user can easily perform his/her makeup or maintenance according to the assistance from the analyzing apparatus 1.
The input unit 13 is arranged at one side of the analyzing apparatus 1, and can be a physical style unit or a touch style unit. By using the input unit 13, the user is allowed to interact with the analyzing apparatus 1, so as to operate the analyzing apparatus 1 and instruct the same. For example, the user can select different makeup areas (such as blush areas, foundation areas, etc.) on the face image to be labeled by the analyzing apparatus 1, or switch the makeup steps/makeup suggestions provided by the analyzing apparatus 1 (such as page-up, page-down, etc.).
In one embodiment, the display 11 is a touch screen which can be operated directly by the user. In this embodiment, the input unit 13 and the display 11 are integrated into one component and not existed individually.
The wireless transmission unit 14 is utilized to connect to the Internet, so the analyzing apparatus 1 can connect to a remote electronic device or server through the Internet. In the present invention, the analyzing apparatus 1 utilizes one or more algorithms to analyze and evaluate the user's facial muscle status of the face in the face image, a d those algorithms and corresponding database can be alternatively stored in the analyzing apparatus 1 or the remote electronic device/server, not limited thereto. Besides, the user can operate a user terminal (not shown) to connect to the analyzing apparatus 1 through network, so as to perform firmware maintenance and firmware updating to the analyzing apparatus 1 from a remote place.
In one embodiment, the analyzing apparatus 1 captures user's face image in real-time through the image capturing unit 12, and analyzes the face image for evaluating user's current facial muscle status. In other embodiment, the analyzing apparatus 1 downloads user's face image pre-shot before from the remote electronic device or server through the Internet, and analyzes facial features in the face image for evaluating user's facial muscle status back at the time the user took this face image.
The storage 15 stores algorithms and database(s) utilized by the analyzing apparatus 1 in performing the evaluating method of the present invention. In particular, the storage 15 stores at least an analyzing algorithm 151, a fuzzy comparison algorithm 152, and a training model 153, but not limited thereto. As discussed above, the analyzing algorithm 151, the fuzzy comparison algorithm 152, and the training model 153 can also be stored in the remote electronic device or remote server, and the analyzing apparatus 1 can access the remote electronic device or the remote server through the Internet for executing the analyzing algorithm 151, the fuzzy comparison algorithm 152, and the training model 153 remotely existed in order to implement the evaluating method of the present invention.
In other embodiment, the analyzing algorithm 151 and the fuzzy comparison algorithm 152 can be embedded in the processor 10 as a part of firmware of the processor 10, but not limited thereto.
In the present invention, a manufacturer of the analyzing apparatus 1 can pre-import a bunch of unspecified face images into a database, and manually labels multiple facial muscle identifying points defined by professors (such as doctors, cosmetologist, etc.) respectively on each of the face images, so as to train and create the aforementioned training model 153. In one embodiment, the multiple facial muscle identifying points manually labeled on each of the face images are respectively corresponding to the positions of risorius muscle and masticatory muscle of a face covered in the face image.
One of the main technical features of the present invention is that, when obtaining a new face image, the analyzing apparatus 1 first performs an image analysis process to the face image through an algorithm for calculating the positions that multiple facial muscle identifying points supposed to be (i.e., the ideal positions) on a face in the face image according to the positions of five sense features of the face. Also, the analyzing apparatus 1 imports the same face image into the training model 153 for identifying the positions that multiple facial muscle identifying points actually exist (i.e., the actual positions) on the face in the face image. Therefore, the analyzing apparatus 1 is able to use the difference between the ideal positions and the actual positions of the multiple facial muscle identifying points to evaluate the muscle status of the face covered in the face image.
Refers to
In the embodiment of
Within a certain range, the muscle status of the user 2 is determined better (for example, younger or tighter) if the positions of the first actual muscle identifying point 21 and the second actual muscle identifying point 22 are close to the inner side of the face (e.g., close to the nose) as well as the upper side of the face (e.g., close to the eyes). In one embodiment, the analyzing apparatus 1 can simulate a first virtual triangle constituted by the first actual muscle identifying point 21, a position of nasion, and a position of right temple as well as a second virtual triangle constituted by the second actual muscle identifying point 22, the position of nasion, and a position of right temple. According to the aforementioned disclosure, within a certain range, the muscle status of the user 2 is determined better once the square measures of the first and second virtual triangles are smaller, in other words, the square measures of the first and second virtual triangles are inversely proportional to the muscle status of the user 2.
Similarly, within a certain range, the muscle status of the user 2 is determined better if the positions of the third actual muscle identifying point 23 and the fourth actual muscle identifying point 24 are close to the inner side of the face (e.g., close to the mouse) as well as the upper side of the face (e.g., close to the nose). In one embodiment, the analyzing apparatus 1 can simulate a third virtual triangle constituted by the third actual muscle identifying point 23, a position of nose tip, and a position of right cheek as well as a fourth virtual triangle constituted by the fourth actual muscle identifying point 24, the position of nose tip, and a position of left cheek. Accordingly, within a certain range, the muscle status of the user 2 is determined better once the square measures of the third and fourth virtual triangles are smaller, in other words, the square measures of the third and fourth virtual triangles are also inversely proportional to the muscle status of the user 2.
In the present invention, the analyzing apparatus 1 utilizes the processor 10 to control the image capturing unit 12 to shoot at least one photo, and the processor 10 executes an image analysis process to the photo to determine whether a face image covering the user's face is existed in the photo. In one embodiment, the processor 10 retrieves the face image from the photo for executing following analyzing and evaluation steps only if the face image is determined existed in the photo and the size of the face image is determined larger than a certain ratio of the entire photo (for example, exceeds 50% or 60% of the photo). If the face image is absent from the photo or existed in the photo without sufficient size, the processor 10 neither analyze nor evaluate the face image, and will control the display 11 to display a message for asking the user to re-take another photo.
In other embodiment, the analyzing apparatus 1 can receive at least one face image from a remote end through the wireless transmission unit 14 after being activated, and performs following analyzing and evaluation steps to the received face image. In this embodiment, it is unnecessary for the processor 10 to enable the image capturing unit 12.
After the step S14, the processor 10 executes the analyzing algorithm 151 to perform an image analysis process to the retrieved or received face image, so as to obtain ideal positions of multiple facial muscle identifying points (referred to as the ideal facial muscle identifying points) from the face in the face image (step S16). In the present invention, the multiple ideal muscle identifying points (such as multiple ideal muscle identifying points 41-44 as shown in
Specifically, parameters like human's five sense features and bones' positions, sizes, and distributed ratio on the face, etc., are unlikely changed due to external factors (such as climate) and time factor, which should be categorized as relatively stable features upon human's face. In the present invention, the analyzing algorithm 151 calculates the ideal positions of the multiple facial muscle identifying points of the face in the face image based on parameters such as positions, sizes, and distributed ratio of the five sense features of the face, in other words, if the user is well maintained, the positions of the multiple actual muscle identifying points 21-24 on the face of the user should be approximate to the positions of the multiple ideal muscle identifying points 41-44, or even overlapped with parts or all of the multiple ideal muscle identifying potions 41-44.
After the step S14, the processor 10 simultaneously identifies the retrieved or received face image through the fuzzy comparison algorithm 152 and the training model 153, so as to obtain actual potions of multiple facial muscle identifying points (i.e., the multiple actual muscle identifying points 21-24) from the face in the face image (step S18). In the present invention, the multiple actual muscle identifying points 21-24 are indicating the actual muscle status of the face in the face image.
In comparison with the five sense features and the bones, human's muscle is likely changed (for example, get drooping) due to external factors and time factor. In the present invention, the training model 153 records multiple reference images (which are multiple unspecified face images as discussed above), and these reference images are respectively pre-labeled, by a provider through manual manner, with multiple muscle identifying points thereon (which are considered as multiple actual muscle identifying points of a face covered in each of the reference images).
In the step S18, the processor 10 executes the fuzzy comparison algorithm 151 to perform a fuzzy comparison process to the face image with multiple reference images (not shown) of the training model 153 for obtaining at least one reference image out of the multiple reference images which is determined approximate to the face image. Eventually, the processor 10 deems the positions of multiple muscle identifying points labeled on the obtained reference image as the positions of the multiple actual muscle identifying points 21-24 on the face of the face image. For example, the processor 10 can retrieve the coordinates of the multiple labeled muscle identifying points from the obtained reference image, and directly set the positions of the multiple actual muscle identifying points 21-24 on the face of the face image according to the retrieved coordinates.
It can be seen that the fuzzy comparison algorithm 152 is different from the analyzing algorithm 151. In particular, the analyzing algorithm 151 utilizes the five sense features on the face to directly calculate the ideal positions of the multiple facial muscle identifying points. On the other hand, the fuzzy comparison algorithm 152 utilizes the pre-trained training model 153 to perform a fuzzy comparison process to the face image for obtaining the positions of the multiple actual muscle identifying points 21-24, which is different from the analyzing algorithm 151. More specific, the positions of the multiple actual muscle identifying points 21-24 obtained by the fuzzy comparison algorithm 152 are likely matching with user's current facial muscle status, i.e., the positions of the multiple actual muscle identifying points 21-24 can be used to represent the status of user's facial muscle such as tight or drooping.
In one embodiment, the processor 10 may first execute the step S16 and then execute the step S18, or vice versa. In other embodiment, the processor 10 may execute the step S16 and the step S18 simultaneously through multiplexing, the execution order is not limited thereto.
After the step S16 and the step S18, the processor 10 evaluates each of the actual muscle identifying points 21-24 respectively according to the multiple ideal muscle identifying points 41-44 in company with a pre-stored evaluation rule, and generates an evaluated result (step S20). In the present invention, the evaluated result can be textual descriptions (for example, “poor”, “fair”, “average”, “good”, “excellent”, etc.) of scores (for example, “91˜100 points”, “81˜90 points”, “71˜80 points”, etc.) for each of the actual muscle identifying points 21-24, or the ratio (also called as performance rate) of each of the actual muscle identifying points 21-24 in comparison with each of the ideal muscle identifying points 41-44. However, the above description is only one of the exemplary embodiments of the present invention, not limited thereto.
In one embodiment, the evaluation rule is to apply a grid matrix onto the face image for calculating a distance between each of the actual muscle identifying points 21-24 and each of the ideal muscle identifying points 41-44 according to the grid matrix, so as to evaluate each of the actual muscle identifying points 21-24 based on the calculated distance. In other words, the evaluation rule uses each of the ideal muscle identifying points 41-44 as a foundational point and evaluates each of the actual muscle identifying points 21-24 based on such distance and foundational point.
After the step S20, the processor 10 controls the display 11 to display the aforementioned face image, multiple actual muscle identifying points 21-24, and multiple ideal muscle identifying points 41-44 (step S22), so the user can see the distances between the multiple actual muscle identifying points 21-24 and the multiple ideal muscle identifying points 41-44 directly on the display 11. Also, the processor 10 controls the display 11 to display the evaluated result (step S24), so the user can be aware of his/her current facial muscle status. In one embodiment, the processor 10 controls the display 11 to display the multiple actual muscle identifying points 21-24 as well as the multiple ideal muscle identifying points 41-44 overlapped with the face image, which applies a visible approach to directly show the distances between the multiple actual muscle identifying points 21-24 and the multiple ideal muscle identifying points 41-44 upon the face on the display 11.
After the step S18 as shown in
After the above discussed actions are executed completely, the processor 10 can generate at least four evaluation results, wherein the at least four evaluation results include a first evaluation result representing the distance between the first actual muscle identifying point 21 and the first ideal muscle identifying point 41, a second evaluation result representing the distance between the second actual muscle identifying point 22 and the second ideal muscle identifying point 42, a third evaluation result representing the distance between the third actual muscle identifying point 23 and the third ideal muscle identifying point 43, and a fourth evaluation result representing the distance between the fourth actual muscle identifying point 24 and the fourth ideal muscle identifying point 44. As a result, the user can be clearly aware of the difference between his/her current facial muscle status and an ideal muscle status recommended to his/her face, so as to determine whether the currently applied care products, cosmetics, or maintenance manners are effective or not.
As discussed, the analyzing algorithm 151 in the step S16 of
Please refer to
In the present invention, the processor 10 performs an image identification process to the face image after obtaining the face image, so as to retrieve at least the positions of right eye, left eye, nose, and lips (step S160). In one embodiment, the processor 10 utilizes Dlib Face Landmark system to perform the image identification process to the face image for obtaining the positions of right eye, left eye, nose, and lips from the face in the face image.
Specifically, the Dlib Face Landmark system can identify outstanding points from a face (around 119 points), and constitutes outstanding features of the face, such as eyebrows, eyes, nose, lips, contours of cheeks, contour of jaw, etc., according to these identified outstanding points. By performing the image identification process to the face image through the Dlib Face Landmark system, the processor 10 can obtain one or more outstanding features which are essential for the upcoming analysis from the face in the face image.
After the step S160, the processor 10 connects an inner corner of an eye with an outer corner of the eye for virtually generating a reference line (step S162). In particular, as shown in
Next, the processor 10 respectively segments each of the reference lines into four equal parts logically (step S164), and respectively generates another reference line perpendicular to such reference line from a position corresponding to a first part out of the four equal parts which is close to the outer corner of the eye (step S166). In particular, as shown in
Next, the processor 10 logically segments a height from the nose ala (e.g., a position close to an outer side of the nose ala) to the nose tip of the face into five equal parts (step S168), and regards a point upon each of the vertical reference lines which is located at a position corresponding to a second part out of the five equal parts counted downward from the nose ala as the ideal muscle identifying point which corresponds to the ideal position of risorius muscle of the face (step S170).
In particular, as shown in
Next, the processor 10 logically segments a height from the nose tip to the jaw (e.g., the outline of the jaw) into three equal parts (step S172), and regards a point upon each of the vertical reference lines which is located at a position corresponding to a first part out of the three equal parts counted downward from the nose tip as the ideal muscle identifying point which corresponds to the ideal position of masticatory muscle of the face (step S174).
In particular, as shown in
It is worth saying that the processor 10 in the aforementioned embodiment is to first calculate the first ideal muscle identifying point 41 and the third ideal muscle identifying point 43 at right side of the face in the face image, and then calculates the second ideal muscle identifying point 42 and the fourth ideal muscle identifying point 44 at left side of the face in the face image. In other embodiment, however, the processor 10 may first calculate the first ideal muscle identifying point 41 and the second ideal muscle identifying point 42 corresponding to risorius muscle of the face, and then calculates the third ideal muscle identifying point 43 and the fourth ideal muscle identifying point 44 corresponding to masticatory muscle of the face. Furthermore, the processor 10 may calculate the four ideal muscle identifying points 41-44 simultaneously through multiplexing, not limited to the above embodiments.
As shown in
Please refer to
In one embodiment, the processor 10 evaluates the status of the first actual muscle identifying point 21 and the second actual muscle identifying point 22 as “average” when the distances between the positions of the two actual muscle identifying points 21-22 and the positions of the first ideal muscle identifying point 41 and the second ideal muscle identifying point 42 are smaller than or equal to a threshold (for example, smaller than a height of one black), evaluates the status of the first actual muscle identifying point 21 and the second actual muscle identifying point 22 as “good” or “excellent” when the positions of the two actual muscle identifying points 21-22 are higher than the positions of the first ideal muscle identifying point 41 and the second ideal muscle identifying point 42 and the distances between the positions of the two actual muscle identifying points 21-22 and the positions of the two ideal muscle identifying points 41-42 are bigger than the threshold. For example, the processor 10 can evaluate the status of the two actual muscle identifying points 21-22 as “good” if the positions of the two actual muscle identifying points 21-22 are higher than the positions of the two ideal muscle identifying points 41-42 by a height of one block, and evaluates the status of the two actual muscle identifying points 21-22 as “excellent” if the positions of the two actual muscle identifying points 21-22 are higher than the positions of the two ideal muscle identifying points 41-42 by a height of at least two blocks.
Further, the processor 10 can evaluate the status of the first actual muscle identifying point 21 and the second actual muscle identifying point 22 as “fair” or “poor” when the positions of the two actual muscle identifying points 21-22 are lower than the positions of the first ideal muscle identifying point 41 and the second ideal muscle identifying point 42 and the distances between the positions of the two actual muscle identifying points 21-22 and the positions of the two ideal muscle identifying points 41-42 are bigger than the threshold. For example, the processor 10 can evaluate the status of the two actual muscle identifying points 21-22 as “fair” if the positions of the two actual muscle identifying points 21-22 are lower than the positions of the two ideal muscle identifying point 41-42 by a height of one to two blocks, and evaluates the status of the two actual muscle identifying points 21-22 as “poor” if the positions of the two actual muscle identifying points 21-22 are lower than the positions of the two ideal muscle identifying points 41-42 by a height of three or more blocks.
In other embodiment as shown in
In this embodiment, the processor 10 can evaluate the status of the two actual muscle identifying points 21-22 as “average” if the distances between the two actual muscle identifying points 21-22 and the two ideal muscle identifying points 41-42 are smaller than or equal to one part out of the five equal parts, evaluates the status of the two actual muscle identifying points 21-22 as “good” if the two actual muscle identifying points 21-22 are higher than the two ideal muscle identifying points 41-42 by one part out of the five equal parts, evaluates the status of the two actual muscle identifying points 21-22 as “excellent” if the two actual muscle identifying points 21-22 are higher than the two ideal muscle identifying points 41-42 by at least two parts out of the five equal parts, evaluates the status of the two actual muscle identifying points 21-22 as “fair” if the two actual muscle identifying points 21-22 are lower than the two ideal muscle identifying points 41-42 by one to two parts out of the five equal parts, and evaluates the status of the two actual muscle identifying points 21-22 as “poor” if the two actual muscle identifying points 21-22 are lower than the two ideal muscle identifying points 41-42 by three or more parts out of the five equal parts.
However, the above descriptions are only a part of the exemplary embodiments of the present invention, not limited thereto.
Please refer to
In this embodiment, the size of each of the intervals of the grid matrix 52 is identical to or different from that of the aforementioned grid matrix 51, not limited thereto.
In this embodiment, the processor 10 can evaluate the status of the third actual muscle identifying point 23 and the fourth actual muscle identifying point 24 as “excellent” if the distances between the positions of the two actual muscle identifying points 23-24 and the positions of the third ideal muscle identifying point 43 as well as the fourth ideal muscle identifying point 44 are smaller than or equal to a threshold (e.g., smaller than a height of one block). Also, the processor 10 can evaluate the status of the third actual muscle identifying point 23 and the fourth actual muscle identifying point 24 as “average”, “fair”, or “poor” in an order if the positions of the two actual muscle identifying points 23-24 are lower than the positions of the two ideal muscle identifying points 43-44 by different distances larger than the threshold.
For example, the processor 10 can evaluate the status of the third actual muscle identifying point 23 and the fourth actual muscle identifying point 24 as “good” if the positions of the two actual muscle identifying points 23-24 are lower than the positions of the two ideal muscle identifying points 43-44 by a height of one block, evaluates the status of the third actual muscle identifying point 23 and the fourth actual muscle identifying point 24 as “average” if the positions of the two actual muscle identifying points 23-24 are lower than the positions of the two ideal muscle identifying points 43-44 by a height of two blocks, evaluates the status of the third actual muscle identifying point 23 and the fourth actual muscle identifying point 24 as “fair” if the positions of the two actual muscle identifying points 23-24 are lower than the positions of the two ideal muscle identifying points 43-44 by a height of three to four blocks, and evaluates the status of the third actual muscle identifying point 23 and the fourth actual muscle identifying point 24 as “poor” if the positions of the two actual muscle identifying points 23-24 are lower than the positions of the two ideal muscle identifying points 43-44 by a height of five or more blocks.
In other embodiment as shown in
In this embodiment, the processor 10 can evaluate the status of the two actual muscle identifying points 23-24 as “excellent” if the distances between the two actual muscle identifying points 23-24 and the two ideal muscle identifying points 43-44 are smaller than or equal to one part out of the fifteen equal parts, evaluates the status of the two actual muscle identifying points 23-24 as “good” if the two actual muscle identifying points 23-24 are lower than the two ideal muscle identifying points 43-44 by at least one part out of the fifteen equal parts, evaluates the status of the two actual muscle identifying points 23-24 as “average” if the two actual muscle identifying points 23-24 are lower than the two ideal muscle identifying points 43-44 by at least two parts out of the fifteen equal parts, evaluates the status of the two actual muscle identifying points 23-24 as “fair” if the two actual muscle identifying points 23-24 are lower than the two ideal muscle identifying points 43-44 by three to four parts out of the fifteen equal parts, and evaluates the status of the two actual muscle identifying points 23-24 as “poor” if the two actual muscle identifying points 23-24 are lower than the two ideal muscle identifying points 43-44 by five or more parts out of the fifteen equal parts.
However, the above description is only one of the exemplary embodiments of the present invention, not limited thereto.
Throughout the aforementioned evaluation rule, the processor 10 can instantly and accurately evaluate the facial muscle status of the user 2 right after the multiple actual muscle identifying points 21-24 as well as the multiple ideal muscle identifying points 41-44 are obtained from the face of the face image.
Please refer to
As disclosed in
In the present invention, the processor 10 can calculate a square measure of a first triangular region 210 virtually constituted by the first actual muscle identifying point 21, the position of nasion, and the position of right side temple of the face in the face image (exampled as 51.3 mm2 in
In one embodiment, the processor 10 can further calculate a square measure of a first ideal triangular region (not shown in the FIGs) virtually constituted by the first ideal muscle identifying point 41, the position of nasion, and the position of right side temple of the face (e.g., 46.2 mm2 as shown in
As discussed above, within a certain range, the status of user's facial muscle will be evaluated better once the square measures of the triangular regions 210-240 are getting smaller. Besides, the processor 10 can calculate the distances between square measure of each of the triangular regions 210-240 and the square measure of each of the ideal triangular regions, so as to further calculate a performance rate corresponding to the maintenance manner(s) currently applied by the user. In other words, once the square measure of each of the triangular regions 210-240 is determined approximate to the square measure of each of the ideal triangular regions, it means the performance rate of the maintenance manners applied by the user is getting higher.
By utilizing the technical solution provided in the disclosures, a user can be aware of the evaluation of his/her current facial muscle status in comparison with an ideal one, so as to determine whether the currently applied care products/cosmetics/maintenance manners are effective or not, which is convenient and irreplaceable.
As the skilled person will appreciate, various changes and modifications can be made to the described embodiment. It is intended to include all such variations, modifications and equivalents which fall within the scope of the present invention, as defined in the accompanying claims.
Number | Date | Country | Kind |
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108144947 | Dec 2019 | TW | national |