Patent Publication Number: US-2021174063-A1

Title: Method for automatically marking muscle feature points on face

Description:
BACKGROUND OF THE INVENTION 
     1. Technical Field 
     The invention relates to a marking method of a face, and specifically to a marking method for marking muscle feature points on a face. 
     2. Description of Related Art 
     Human&#39;s muscle (especially face muscle) will slowly slacken and droop while people age, and some users choose to use care products to maintain their muscle and skin, use cosmetics to cover the slackened muscle, or work out for slowing down the speed of muscle slackening. 
     General speaking, users will be sifting 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 for improving the speed and the quality of using the same. 
     However, the above devices can only assist the users in using the care products/cosmetics, but it cannot actively analyze user&#39;s muscle status. Therefore, users cannot be aware of whether the care products/cosmetics are working after using it for a period of time. 
     According to the above problem, a newly method should be provide in the field for effectively analyzing user&#39;s facial image and automatically marking a plurality of muscle feature points on the facial image for indicating user&#39;s current muscle status, so the user can easily realize his/her current muscle status and determines whether the currently applied care products/cosmetics are effective. 
     SUMMARY OF THE INVENTION 
     The invention is directed to a method for automatically marking muscle feature points on a face, which can automatically mark a plurality of muscle feature points on user&#39;s facial image for indicating user&#39;s current facial muscle status. 
     In one of the exemplary embodiments, the method of the present invention is applied to a facial image analyzing device and includes following steps of: obtaining a to-be-identified image, wherein the to-be-identified image at least includes a face of a user; performing a face recognition procedure to the to-be-identified image for obtaining multiple strong reference points on the face; performing a fuzzy comparison procedure on the to-be-identified image based on a pre-trained training model for generating a comparison result; automatically marking multiple muscle feature points on the to-be-identified image according to the comparison result, wherein the multiple muscle feature points respectively locate at multiple weak reference points of the face; and, displaying the to-be-identified image and the multiple muscle feature points overlapped on the to-be-identified image by a display unit. 
     In comparison with related art, the present invention can identify user&#39;s facial image and automatically mark at least four muscle feature points on user&#39;s face, where the marked muscle feature points can be used to indicate user&#39;s facial muscle status. Therefore, user can be quickly aware of his/her current facial muscle status through the marked and displayed muscle feature points, so as to check whether the care products/cosmetics currently used are effective or not. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a schematic diagram of a facial image analyzing device according to a first embodiment of the present invention. 
         FIG. 2  is a block diagram of the facial image analyzing device according to a first embodiment of the present invention. 
         FIG. 3  is a schematic diagram showing muscle feature points according to a first embodiment of the present invention. 
         FIG. 4  is a flowchart for establishing a training model according to a first embodiment of the present invention. 
         FIG. 5  is a schematic diagram showing the marked location of the muscle feature points according to a first embodiment of the present invention. 
         FIG. 6  is a schematic diagram showing training data according to a first embodiment of the present invention. 
         FIG. 7  is an identification flowchart according to a first embodiment of the present invention. 
         FIG. 8  is an identification flowchart according to a second embodiment of the present invention. 
         FIG. 9  is a schematic diagram showing a training model according to a first embodiment of the present invention. 
         FIG. 10  is a schematic diagram showing the adjustment of the feature points according to a first embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The invention is directed to a method for automatically marking muscle feature points on a face, which can automatically mark a plurality of muscle feature points on user&#39;s facial image for indicating user&#39;s current facial muscle status. 
     Please refer to  FIG. 1  and  FIG. 2 , wherein  FIG. 1  is a schematic diagram of a facial image analyzing device according to a first embodiment of the present invention, and  FIG. 2  is a block diagram of the facial image analyzing device according to a first embodiment of the present invention. 
     A method for automatically marking muscle feature points on face is disclosed by the present invention (referred to as the marking method hereinafter), and the marking method is mainly applied to a facial image analyzing device  1  (referred to as the analyzing device  1  hereinafter) as disclosed in  FIG. 1  and  FIG. 2 . Specifically, one of the technical features of the present invention is to obtain user&#39;s facial image through the analyzing device  1 , then identifies the facial image based on a pre-trained Artificial Intelligent (AI) model, and then marks multiple muscle feature points on the facial image for indicating user&#39;s current facial muscle status according to an identification result. By automatically marking the multiple muscle feature points through the analyzing device  1 , users can be quickly and effectively aware of his/her current facial muscle status, so as to estimate whether the currently used care products/cosmetics and maintenance manners (such as working out or aesthetic medicine) are effective or not. 
     The analyzing device  1  shown in  FIG. 1  and  FIG. 2  is mainly used to assist the user in applying care products and cosmetics. More specific, the analyzing device  1  may provide makeup suggestions before the user start to apply cosmetics, provides makeup assistance while the user is making up, and provides analysis and evaluation for user&#39;s makeup after the user finishes applying cosmetics. It should be mentioned that, if other electronic devices, such as smart phones, laptops, etc., have same or similar hardware as the analyzing device  1  and are installed with corresponding software for performing each control step of the marking method of the present invention, the marking method of the present invention, therefore, can no longer be limited running on the analyzing device  1  of  FIG. 1  and  FIG. 2 . 
     As shown in  FIG. 1  and  FIG. 2 , the analyzing device  1  includes a processor  10 , a display unit  11 , an image capturing unit  12 , an input unit  13 , a wireless transmitting unit  14 , and a storage  15 . The processor  10  is electrically connected with the display unit  11 , the image capturing unit  12 , the input unit  13 , the wireless transmitting unit  14 , and the storage  15  through serial bus, so as to integrate and control all of these components. 
     The analyzing device  1  can capture user&#39;s photo in real-time through the image capturing unit  12 , then retrieves user&#39;s facial image from the photo and displays the facial image on the display unit  11 . In one of the embodiments, the image capturing unit  12  is basically utilized to capture a photo including user&#39;s face. One of the plurality of technical features of the present invention is that the analyzing device  1  can identify the retrieved facial image and automatically marks a plurality of muscle feature points on the facial image for indicating user&#39;s current facial muscle status, and can also display simultaneously the facial image as well as the plurality of muscle feature points on the display unit  11 . In a conclusion, user can be quickly aware of his/her current muscle status through the information displayed on the analyzing device  1 . 
     The input unit  13  is arranged at one side of the analyzing device  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 device  1 , so as to operate the analyzing device and instruct the same. For example, the user can select different functions on the analyzing device  1  through operating the input unit  13  (such as a makeup assistance function, a facial muscle analysis function, etc.), or switch the makeup steps/makeup suggestions provided by the analyzing device  1  (such as page-up, page-down, etc.). 
     In one embodiment, the display unit  11  is a touch screen which can be operated by the user. In this embodiment, the input unit  13  and the display unit  11  are integrated into one component and not individually existed. 
     The wireless transmitting unit  14  is utilized to connect to the Internet, so the analyzing device  1  can connect to a remote electronic device or server through the Internet. In the present invention, the analyzing device  1  utilizes one or more algorithm to perform a training procedure for training an artificial intelligent (AI) model, a recognition procedure for recognizing the facial image, and a marking procedure for marking the muscle feature points. These algorithms and the AI model trained can be stored in the analyzing device  1  or the remote electronic device and server, not limited thereto. In addition, the user can operate a user terminal (not shown) to connect to the analyzing device  1  through network, so as to perform firmware maintenance and firmware updating to the analyzing device  1  from a remote place. 
     In one embodiment, the analyzing device  1  utilizes the image capturing unit  12  to capture user&#39;s facial image in real-time, and identifies the facial image to analyze user&#39;s current facial muscle status. In other embodiment, the analyzing device  1  downloads user&#39;s photo from a remote electronic device or server, and identifies the facial image included in the download photo for evaluating the facial muscle status of the user by the time the user took this photo. In another embodiment, the analyzing device  1  reads the user&#39;s photo from the storage  15 , and identifies the facial image included in the photo for evaluating the facial muscle status of the user by the time the user took this photo. 
     The storage  15  stores the algorithms and the model(s) utilized to perform the marking method of the present invention. In particular, the storage  15  stores at least a training model  153  pre-trained for performing a fuzzy comparison procedure, and an AI training algorithm  151  as well as a bunch of training data  152  for training the training model  153 , but not limited thereto. The AI training algorithm  151 , the training data  152  as well as the training model  153  can also be stored in the remote electronic device or server, and the analyzing device  1  can connect to the remote electronic device or server through network for accessing the AI training algorithm  151 , the training data  152 , and the training model  153  from a remote place. 
     In another embodiment, the AI training algorithm  151  can be embedded in the processor  10  for being a part of the firmware of the processor  10 , but not limited thereto. 
     In the present invention, the manufacturer of the analyzing device  1  can pre-import a bunch of training data  152  into the storage  15  of the analyzing device  1 . The multiple records of training data  152  here indicate facial images of unspecified persons, and each of the facial images has been manually marked thereon with a plurality of muscle feature points defined by professionals such as doctors, cosmetologists, etc. Therefore, the analyzing device  1  can perform a training procedure to train the training model  153  through executing the AI training algorithm  151  based on the multiple records of the training data  152 . In this invention, the locations of the plurality of muscle feature points distributed on each of the facial images can be used to represent the muscle status of the face. 
     One of the technical features of the present invention is that, when obtaining a new photo (a facial image captured by the image capturing unit  12  or a photo read from the storage  15 ), the analyzing device  1  can perform a fuzzy comparison procedure to the photo based on the training model  153 , and automatically marks a plurality of muscle feature points on a facial image included in the photo according to a result of the fuzzy comparison procedure. Therefore, the user can determine his/her current facial muscle status according to the locations of the plurality of muscle feature points on the facial image automatically marked by the analyzing device  1 . 
       FIG. 3  is a schematic diagram showing muscle feature points according to a first embodiment of the present invention. As disclosed in  FIG. 3 , after obtaining a to-be-identified image  2  which includes user&#39;s facial image through the image capturing unit  12 , the wireless transmitting unit  14 , or the storage  15 , the analyzing device  1  can perform the fuzzy comparison procedure to the to-be-identified image  2  according to the pre-trained training model  153  for finding the locations for a plurality of muscle feature points  3  distributed on the facial image of the to-be-identified image  2 , so as to mark the plurality of muscle feature points  3  on the facial image of the to-be-identified image  2  and then displays the muscle feature points  3  as well as the to-be-identified image  2  on the display unit  11 . 
     In the embodiment shown in  FIG. 3 , the analyzing device  1  automatically marks at least four muscle feature points  3  on the to-be-identified image  2  according to the training model  153 , the at least four muscle feature points  3  includes a first muscle feature point  31  located at upper-left of the face (corresponding to a location of left risorius muscle), a second muscle feature point  32  located at lower-left of the face (corresponding to a location of left masticatory muscle), a third muscle feature point  33  located at upper-right of the face (corresponding to a location of right risorius muscle), and a fourth muscle feature point  34  located at lower-right of the face (corresponding to a location of right masticatory muscle). The amount of the plurality of muscle feature points  3  illustrated in  FIG. 3  is four, however, it can be more than four in other embodiments. 
     It should be mentioned that within a certain range, the user&#39;s muscle status is regarded great once the first muscle feature point  31  as well as the third muscle feature point  33  are close to inner side and upper side of the face (i.e., close to nose and eyes). Similarly, within a certain range, the user&#39;s muscle status is regarded great once the second muscle feature point  32  as well as the fourth muscle feature point  34  are close to inner side and upper side of the face (i.e., close to mouth and nose). 
     As mentioned above, the marking method of the present invention is to identify the to-be-identified image  2  through the pre-trained training model  153 , and automatically marks at least four muscle feature points  3  on the to-be-identified image  2  after the identification procedure. Therefore, a technical effect can be achieved that the user can check his/her current muscle status according to the locations of the at least four muscle feature points  3  marked. 
     In comparison with multiple strong reference points on a face (such as organs like eyes, nose, mouth, or facial parts with obvious characteristics), the muscle feature points  3  defined in the present invention are mainly corresponding to multiple weak reference points on the face. As a result, before utilizing the analyzing device  1  to implement the marking method of the present invention which automatically marks such muscle feature points  3  on a to-be-identified image  2  input by the user, the training model  153  needs to be pre-established for the analyzing device  1  to perform such fuzzy comparison procedure. 
     Please refer to  FIG. 4 , which is a flowchart for establishing a training model according to a first embodiment of the present invention, and is illustrated to specifically describe an establishing procedure of the training model  153  of the present invention. 
     As shown in  FIG. 4 , firstly, the user obtains a bunch of records of the training data  152 , these records of the training data  152  respectively indicates different facial image of different people. Next, the user manually marks at least four muscle feature points on each of the facial image of each record of the training data  152  according to the facial muscle status represented by each facial image based on one or more feature point setting rules defined by professionals such as doctors or cosmetologist (step S 10 ). As discussed before, the present invention utilizes the at least four muscle feature points to represent the muscle status of a face, in other words, in a previous training phase the user can manually mark multiple muscle feature points on each of the records of the training data  152  according to the real muscle status represented by each face in each of the records of the training data  152 , and regards these marked records of training data  152  as a training foundation when establishing the training model  153 . 
       FIG. 5  is a schematic diagram showing the marked location of the muscle feature points according to a first embodiment of the present invention. In the present invention, the manually marked muscle feature points  4  includes a first muscle feature point  41  and a second muscle feature point  42  located at left side of a face, and includes a third muscle feature point  43  and a fourth muscle feature point  44  located at right side of the face. In the embodiment, the at least four muscle feature points  4  are respectively located at four weak reference points of the face, in other words, an amount of the weak reference points is corresponding to an amount of the muscle feature points  4  marked. In particular, even if the amount of the muscle feature points  4  the user marked on each facial image included in each record of the training data  152  is greater than four, each of the muscle feature points  4  should be still marked at a weak reference point of the face image of each record of the training data  152 . 
     More specific, the user may perform analysis and determination to the facial image of each record of the training data  152  by bare eyes or algorithm, so as to obtain multiple area constituting assistance lines including a first tear trough tangent  61 , a first nasolabial fold tangent  63 , a first marionette line tangent  65 , a first vertical line of eye corner  67 , and a first mandible ramus tangent  69  on left side of the face as well as multiple area constituting assistance lines including a second tear trough tangent  62 , a second nasolabial fold tangent  64 , a second marionette line tangent  66 , a second vertical line of eye corner  68 , and a second mandible ramus tangent  70  on right side of the face according to the real muscle status represented by the face inside of each record of the training data  152 . In the present invention, the user may decide specific locations for the muscle feature points  4  according to such area constituting assistance lines, and then directly and manually marks the at least four muscle feature points  4  on each record of the training data  152 . 
     When marking such muscle feature points  4 , the user should make the first muscle feature point  41  to be located and marked within a region constituted by the first tear through tangent  61 , the first nasolabial fold tangent  63 , the first vertical line of eye corner  67 , and the first mandible ramus tangent  69  at the left side of the face; make the second muscle feature point  42  to be located and marked within a region constituted by the first nasolabial fold tangent  63 , the first marionette line tangent  65 , the first vertical line of eye corner  67 , and the first mandible ramus tangent  69  at the left side of the face; make the third muscle feature point  43  to be located and marked within a region constituted by the second tear trough tangent  62 , the second nasolabial fold tangent  64 , the second vertical line of eye corner  68 , and the second mandible ramus tangent  70  at the right side of the face; and make the fourth muscle feature point  44  to be located and marked within a region constituted by the second nasolabial fold tangent  64 , the second marionette line tangent  66 , the second vertical line of eye corner  68 , and the second mandible ramus tangent  70  at the right side of the face. The above description is only one of the exemplary embodiments of the present invention, but not limited thereto. 
     In addition, the analyzing device  1  may determine if each of the muscle feature points  4  is marked within a corresponding region constituted by corresponding area constituting assistance lines, so as to pre-determine whether the muscle feature points  4  manually marked by the user are correct or not, and filters mis-marked muscle feature point(s)  4  or makes an alert for the user to double check. Similarly, the above assistance approach can also be applied in the following procedure after the training model  153  is well-trained, so as to optionally determine whether the muscle feature points  3  automatically marked according to the training model  153  are correct or not. By using such assistance approach, the marking method of the present invention can be effectively assisted when the amount of the records of the training data  152  is not sufficiently enough while the training model  153  is trained. 
     Please refer back to  FIG. 4 . The user initially performs a face recognition procedure to each record of the training data  152  through the analyzing device  1  or other electronic device, so as to find and locate multiple facial features from the record of the training data  152  for being the training foundations. In one embodiment, the analyzing device  1  executes a histogram of oriented gradient (HOG) algorithm of Dlib Face Landmark system by the processor  10  of the analyzing device  1  to perform the face recognition procedure to each record of the training data  152 , and generates a face locating frame on each record of the training data  152  for indicating effective facial features thereon (step S 12 ). In one of the exemplary embodiments, at least a part of the facial features are strong reference points of the face included in each record of the training data  152 . 
     Please also refer to  FIG. 6 , which is a schematic diagram showing training data according to a first embodiment of the present invention. As disclosed in  FIG. 6 , the aforementioned HOG algorithm is executed to transform a photo into multiple vectors, and determines a location of a facial image in the photo according to the combination of the size, direction, shape, etc., of these vectors, and also generates a face locating frame  5  which can cover the whole facial image. In the embodiment, the fourth muscle feature points  4  are all located within the face locating frame  5 , and the connection of the four muscle feature points  4  can virtually form a rectangle frame or a trapezoid frame. 
     Refer back to  FIG. 4 . After the step S 12 , the analyzing device  1  may establish the training model  153  through executing the AI training algorithm  151  according to the training data  152  (step S 14 ). More specific, the AI training algorithm  151  is executed to perform a training procedure based on the multiple records of the training data  152 , the muscle feature points  4  manually marked on each record of the training data  152 , the face locating frame  5  of each record of the training data  152 , and the multiple facial features within the face locating frame  5  of each record of the training data  152 . 
     It should be mentioned that the user may optionally choose to mark these muscle feature points  4  first, or to make the analyzing device  1  to generate the face locating frame  5  first. In other words, the step S 10  and the step S 12  do not have an essential executing order. 
     In the present invention, the AI training algorithm  151  is executed to analyze the training data  152  during the training procedure, and records at least a relationship among multiple feature points within the face locating frame  5 , a relationship among each of the plurality of muscle feature points  4  (such as the size, the shape, the angles of the rectangle frame or the trapezoid shape), a relationship between each muscle feature point  4  and the one or more feature points (especially strong reference points) within the face locating frame  5 , etc. (step S 16 ). 
     It is worth saying that the AI training algorithm  151 , in the step S 16 , can also calculate the probability of each muscle feature point  4  appearing at each location of the face (for example, where are the locations that these muscle feature points  4  are never located) during the training procedure, and also generates a basic locating rule of each of the muscle feature points  4  (for example, the location of the first muscle feature point  41  is must higher than the location of the second muscle feature point  42 , the third muscle feature point  43  is must located at a right side of the first muscle feature point  41 , etc.), and the probability and the basic locating rule can be utilized by the analyzing device  1  as predicted reference values while performing the fuzzy comparison procedure to a to-be-identified image  2  as newly provided. 
     After the step S 16 , the AI training algorithm  151  can generate multiple muscle feature point locating rules according to the corresponding relationships obtained by the analysis performed in the step S 16  (step S 18 ), and then establishes a training model  153  according to the multiple muscle feature point locating rules, a determination depth, and a number of regressions (step S 20 ). After the training model  153  is established, the analyzing device  1  can perform the fuzzy comparison procedure to a to-be-identified image newly inputted, so as to automatically mark the aforementioned multiple muscle feature points  3  on a facial image in the to-be-identified image  2  based on the training model  153 . 
     In the present invention, the training model  153  generated by the AI training algorithm  151  is a type of regressor, which includes multiple Cascade regression trees with same contents. Each of the regression trees included in the regressor (i.e., in the training model  153 ) respectively has multiple determination nodes, and the contents of at least a part of the determination nodes are corresponding to the above muscle feature point locating rules (as shown in  FIG. 9 , detailed described below). 
     It should be mentioned that, before the AI training algorithm  151  is executed, the user may pre-set the number of the regressions and the number of the determination nodes, according to parameters such as hardware performance of the analyzing device  1 , required accuracy of the identification result, processing time acceptable to the user, etc. In the present embodiment, the number of the regression trees equals the pre-set number of regressions, and the pre-set number of determination nodes equals the determination depth. Without concerning the hardware performance, accuracy of the identification result and the processing time, the identification result will be more accurate once the number of regressions and the determination depth are bigger. 
     Please refer to  FIG. 7 , which is an identification flowchart according to a first embodiment of the present invention.  FIG. 7  discloses each execution step of the marking method of the present invention, where the marking method is applied to the analyzing device  1  as shown in  FIG. 1  and  FIG. 2 . 
     When using the analyzing device  1  to identify the muscle status of his/her face, the user imports a to-be-identified image  2  (as shown in  FIG. 3 ) having user&#39;s facial image to the analyzing device  1 , and the analyzing device  1  can obtain the to-be-identified image  2  of the user (step S 30 ). In one embodiment, the analyzing device  1  may real-time capture the to-be-identified image  2  through its image capturing unit  12 . In other embodiment, the analyzing device  1  can read the to-be-identified image  2  pre-stored by the user from the storage  15 . In another embodiment, the analyzing device  1  may wirelessly receive the to-be-identified image  2  from the outside through the wireless transmitting unit  14 . 
     After obtaining the to-be-identified image  2 , the analyzing device  1  performs the face recognition procedure to the to-be-identified image  2  through the processor  10 , and the face locating frame  5  as shown in  FIG. 6  is then generated according to the result of the face recognition procedure (step S 32 ). The face locating frame  5  indicates a certain facial image included in the to-be-identified image  2 , in particular, the face locating frame  5  covers multiple strong reference points, such as organs like eyes, nose, and mouth, or obvious characteristic points, upon the facial image included in the to-be-identified image  2 . 
     In one embodiment, the processor  10  performs the face recognition procedure to the to-be-identified image  2  through the HOG algorithm of Dlib Face Landmark system as mentioned above, and generate such face locating frame  5  on the to-be-identified image  2  for indicating the facial image of the user. In the present invention, the processor  10  only perform training for the training model  153  according to multiple feature points within the face locating frame  5  of each record of the training data  152 . As a result, when performing the identification procedure, the processor  10  will only perform identification according to multiple feature points within the face locating frame  5  of the to-be-identified image  2 . 
     After the step S 32 , the processor  10  performs fuzzy comparison procedure to the to-be-identified image  2  based on the training model  153  and then generates an identification result (step S 34 ). Therefore, the processor  10  can mark the locations of four muscle feature points  3  on the to-be-identified image  2  according to the identification result (step S 36 ). In the present invention, the four muscle feature points  3  will be located within the face locating frame  5  of the to-be-identified image  2  and respectively corresponded to at least four weak reference points upon use&#39;s facial image in the to-be-identified image  2 . 
     After the step S 36 , the processor  10  controls the display unit  11  to display the to-be-identified image  2  as well as the four muscle feature points  3  (step S 38 ), and the four muscle feature points  3  are respectively overlapped with a corresponding location on the facial image in the to-be-identified image  2 . 
     In the marking method of the present invention, the processor  10  performs the fuzzy comparison procedure to the to-be-identified image  2  based on the training model  153 , so the locations of the four muscle feature points  3  automatically marked by the processor  10  on the to-be-identified image  2  will be complying with the locating rules pre-analyzed according to the bunch of records of the training data  152 . 
     In particular, an amount of the multiple muscle feature points  3  automatically marked on the to-be-identified image  2  through the fuzzy comparison procedure is corresponding to an amount of the multiple muscle feature points  4  manually marked in each of the records of the training data  152 . In other words, the amount of the muscle feature points  3  marked by the processor  10  in step S 36  and the amount of the muscle feature points  3  displayed on the to-be-identified image  2  in the step S 38 , are corresponding to the amount of the muscle feature points  4  marked in each of the records of the training data  152  stored in the storage  15 , i.e., the amount is plurality, but not limited at four. 
     In one embodiment, the four muscle feature points  3  automatically marked by the processor  10  includes a first muscle feature point  31  and a second muscle feature point  32  located at a left side of the facial image in the to-be-identified image  2 , and also includes a third muscle feature point  33  and a fourth muscle feature point  34  located at a right side of the facial image in the to-be-identified image  2 . In an embodiment, the connection of the four muscle feature points  3  can virtually form a rectangle frame or a trapezoid frame. 
     In other embodiment, the location of the first muscle feature point  31  automatically marked by the processor  10  will be within a region constituted by the aforementioned first tear trough tangent  61 , the first nasolabial fold tangent  63 , the first vertical line of eye corner  67 , and the first mandible ramus tangent  69  on a left side of the facial image; the location of the second muscle feature point  32  will be within a region constituted by the first nasolabial fold tangent  63 , the first marionette line tangent  65 , the first vertical line of eye corner  67 , and the first mandible ramus tangent  69  at the left side of the facial image; the location of the third muscle feature point  33  will be within a region constituted by the second tear trough tangent  62 , the second nasolabial fold tangent  64 , the second vertical line of eye corner  68 , and the second mandible ramus tangent  70  at a right side of the facial image; and the location of the fourth muscle feature point  34  will be within a region constituted by the second nasolabial fold tangent  64 , the second marionette line tangent  66 , the second vertical line of eye corner  68 , and the second mandible ramus tangent  70  at the right side of the facial image. 
     As mentioned above, the present invention provides the analyzing device  1  to automatically mark at least four muscle feature points  3 , so the user can quickly and effectively check his/her current facial muscle status. For example, if the locations of the first muscle feature point  31  and the third muscle feature point  33  automatically marked are close to the eyes of user&#39;s facial image, it means user&#39;s muscle is very tight. For another example, if the locations of the second muscle feature point  32  and the fourth muscle feature point  34  automatically marked are far from the mouth and close to the chin of user&#39;s facial image, it means the user&#39;s muscle is flabby and needed to be maintained. 
     Please refer to  FIG. 8 , which is an identification flowchart according to a second embodiment of the present invention, and is used to detailed describe the specific content of the step S 34  as disclosed in  FIG. 7 . 
     As mentioned above, the training model  153  is a type of regressor which includes multiple Cascade regression trees. As also mentioned before, the AI training algorithm  151  may analyze the bunch of records of the training data  152  and record the probability of each muscle feature point appearing at each location and the basic locating rule of each of the muscle feature points during establishing the training model  153 . Before performing the fuzzy comparison procedure to the to-be-identified image  2  based on the training model  153 , the processor  10  first randomly generates multiple predicted feature points on the to-be-identified image  2  according to the probability and the basic locating rule (step S 340 ). In this embodiment, the multiple predicted feature points are randomly generated and regarded as an initial estimate. The initial estimate can exclude the locations which are impossible for the muscle feature points (e.g., upper of the eyebrow, inside of the mouth, etc.) according to the probability, and ensures the multiple predicted feature points generated are all complied with the basic locating rule (for example, the multiple predicted feature points are all located within the face locating frame  5 , the connection of the multiple predicted feature points can virtually form a rectangle frame or a trapezoid frame, etc.). 
     It is worth saying that the amount of the multiple predicted feature points is corresponding to the amount of the multiple muscle feature points  4  manually marked in each of the records of the training data  152 . In other words, the amount of the multiple predicted feature points randomly generated by the processor  10  in the step S 340  equals the amount of the multiple muscle feature points  4  respectively marked in each of the records of the training data  152  which is used to train the training model  153 . The number is plurality, but not limited at four. 
     Next, the processor  10  imports the to-be-identified image  2  and the multiple predicted feature points to a selected one of plurality of regression trees of the training model  153  (step S 342 ), and obtains a plurality of analyzing results from the selected regression tree (step S 344 ). After the step S 344 , the processor  10  adjusts the multiple predicted feature points respectively according to the plurality of analyzing results obtained from the selected regression tree for generating multiple adjusted-predicted points (step S 346 ). 
     In the present invention, each of the plurality of analyzing results respectively indicates a weight of the combination of the to-be-identified image  2  incorporated with the multiple predicted feature points in comparison with the content of at least a part of the multiple records of the training data  152 . In particular, each weight is utilized to indicate a similarity of a relationship between one or more strong reference points with each of the predicted feature points in the to-be-identified image  2  in comparison with a relationship between one or more strong reference points and each of the muscle feature points  4  in a record of the training data  152 . In other words, the analyzing results can be used to indicate the similarity of a combination of the to-be-identified image  2  incorporated with the current predicted feature points thereon in comparison with a combination of each record of the training data  152  (or the induced/categorized data cluster of the training data  152 ) incorporated with the muscle feature points  4  thereon. 
     In one embodiment, the higher the similarity, the bigger the weight is. In the step S 346 , the processor  10  respectively adjusts the coordinates of each predicted feature point on the to-be-identified image  2  according to different weights (i.e., the plurality of analyzing results), so as to generate a plurality of adjusted-predicted points. The amount of the plurality of adjusted-predicted points equals the amount of the plurality of predicted feature points (such as four in this embodiment). 
     As mentioned above, the training model  153  includes a plurality of regression trees, and the amount of the regression trees is corresponding to the number of regressions preset by the user. After the step S 346 , the processor  10  determines whether the plurality of regression trees of the training model  153  are all executed completely (step S 348 ), i.e., the processor  10  determines if the execution time of the step S 342 , step S 344 , and step S 346  are all equal to the number of regressions preset by the user. 
     In the present invention, before the plurality of regression trees is all executed completely, the processor  10  replaces the previous plurality of predicted feature points with the plurality of adjusted-predicted points generated in the step S 346 , and re-executes the step S 342 , the step S 344 , and the step S 346  in next regression tree of the plurality of regression trees according to the to-be-identified image  2  and the plurality of adjusted-predicted points. When the plurality of regression trees is all executed completely, the processor  10  regards the plurality of adjusted-predicted points generated in the last step S 346  as a plurality of final-confirmed muscle feature points  3 , and the processor  10  outputs the plurality of muscle feature points  3  to terminate the fuzzy comparison procedure (step S 350 ). In the present invention, the multiple muscle feature points  3  automatically marked on the to-be-identified image  2  by the analyzing device  1  are the plurality of final-confirmed muscle feature points  3  output by the processor  10  in the step S 350 . 
     Please refer to  FIG. 9  and  FIG. 10 , wherein  FIG. 9  is a schematic diagram showing a training model according to a first embodiment of the present invention,  FIG. 10  is a schematic diagram showing the adjustment of the feature points according to a first embodiment of the present invention. For easily understanding, four predicted feature points will be illustrated in  FIG. 9  and  FIG. 10  for an example (i.e., four muscle feature points  4  are manually marked in each of the records of the training data  152 ). 
     As shown in the FIGs, before performing the fuzzy comparison procedure, the processor  10  randomly generates four predicted feature points  80  on the to-be-identified image  2  according to the basic locating rule and the probability as described before. It should be noticed that these four predicted feature points  80  merely comply with the basic locating rule and the probability calculated by the AI training algorithm  151  during the training procedure, but cannot represent the actual muscle status of the facial image included in the to-be-identified image  2 . 
     Next, the processor  10  imports the to-be-identified image  2  and the four predicted feature points  80  into a first regression tree  1531  of the plurality of regression trees of the training model  153 . The first regression tree  1531  has multiple determination nodes  1534 , each of the multiple determination nodes  1534  respectively represents one of a plurality of rules, these rules are induced, calculated and obtained by the AI training algorithm  151  during training the training model  153  based on the plurality of records of training data  152 . 
     In the present invention, the content of at least a part of the determination nodes  1534  of the first regression tree  1531  are corresponding to the multiple muscle feature point locating rules generated by the AI training algorithm  151  in the step S 18  as shown in  FIG. 4 , in other words, the processor  10  will determine, at each of the determination nodes  1534 , whether the relationship among each of the four predicted feature points  80  on the to-be-identified image  2  (e.g., the size or the shape of the virtual rectangle frame or a trapezoid frame) is similar to the rule indicated by the content of each of the determination nodes  1534 , or whether the relationship between each of the predicted feature points  80  on the to-be-identified image  2  and the one or more strong reference points within the face locating frame  5  is similar to the rule indicated by the content of each of the determination nodes  1534 . That is to say, the processor  10  determines a YES answer (i.e., similar) or a NO answer (i.e., dissimilar) at each of the determination nodes  1534 . 
     When determining YES or NO at each determination node  1534 , the processor  10  is to basically determine a similar probability and a dissimilar probability, but neither the similar probability nor the dissimilar probability will be 100%. If the plurality of determination nodes  1534  included in the regression tree  1534  are all determined completely, the processor  10  can obtain multiple analyzing results from the plurality of determination nodes  1534 , and each of the multiple analyzing results is respectively corresponding to one of the multiple weights  1535 . 
     In the present invention, each of the obtained weights  1535  is respectively used to indicate the similarity of the relationship between each of the predicted feature points  80  and the one or more strong reference points on the facial image of the to-be-identified image  2  and the relationship between each of the muscle feature points  4  and the one or more strong reference points in each of the records of the training data  152  (or a cluster of multiple similar records of the training data  152 ), or, each of the weights  1535  is also used to respectively indicate the similarity of the relationship among each of the predicted feature points  80  on the facial image of the to-be-identified image  2  and the relationship among each of the muscle feature points  4  in each of the records of the training data  152  (or such cluster). 
     For example, if the facial image in the to-be-identified image  2  is similar to a first type of record of the training data  152  (e.g., indicating a face with a big nose), the corresponding weight  1535  will be higher. If the facial image in the to-be-identified image  2  is dissimilar to a second type of record of the training data  152  (e.g., indicating a face with small eyes), the corresponding weight  1535  will be lower. 
     After all the determination nodes  1534  in the first regression tree  1531  are determined completely and multiple weights  1535  are obtained, the processor  10  further adjusts the coordinates of each of the predicted feature points  80  upon the to-be-identified image  2  according to these weights  1535 , so as to generate a plurality of first adjusted-predicted points  81 . For example, if the facial image of the to-be-identified image  2  is similar to a first type of record of the training data  152  (e.g., indicating a face with a big nose), the coordinates of the first adjusted-predicted points  81  will be adjusted slightly to move toward the locations of the multiple muscle feature points  4  marked in the first type of record of the training data  152 , and the adjusting range of the coordinates is corresponding to the content of the weight  1535  (i.e., the higher the weight  1535 , the bigger the adjusting range will be). 
     As disclosed in  FIG. 10 , the first adjusted-predicted points  81  are closer to the actual locations of the multiple muscle feature points  3  on the facial image of the to-be-identified image  2  in comparison with the predicted feature points  80 , wherein, the amount of the plurality of first adjusted-predicted points  81  equals the amount of the plurality of predicted feature points  80 . 
     Refer back to  FIG. 9 . After obtaining the four first adjusted-predicted points  81 , the processor  10  further imports the to-be-identified image  2  and the four first adjusted-predicted points  81  into a second regression tree  1532  of the training model  153 . In the present invention, the content of the second regression tree  1532  is identical with the content of the aforementioned first regression tree  1531 . Also, the second regression tree  1532  includes multiple determination nodes  1534 , the amount and content of the multiple determination nodes  1534  in the second regression tree  1532  are identical to the amount and content of the multiple determination nodes  1534  in the first regression tree  1531 . However, the imported parameters are changed, from the four predicted feature points  80  to the four first adjusted-predicted points  81 , so the plurality of analyzing results will be different after the second regression tree  1532  is executed completely (i.e., the content of the multiple weights  1535  obtained from the second regression tree  1532  will be different). 
     Similarly, after all the determination nodes  1534  in the second regression tree  1532  are determined completely and multiple weights  1535  are obtained, the processor  10  further adjusts the coordinates of each of the first adjusted-predicted points  81  upon the to-be-identified image  2  according to the obtained weights  1535 , so as to generate multiple second adjusted-predicted points  82 . 
     In the invention, the processor  10  keeps executing above actions (regressions and adjustments). After an N−1 regression tree (not shown) of the training model  153  is executed completely, the processor  10  generates four N−1th adjusted-predicted points  83 . Next, the processor  10  imports the to-be-identified image  2  and the four N−1th adjusted-predicted points  83  into a last regression tree of the training model  153  (an Nth regression tree  1533  in  FIG. 9 ). After the Nth regression tree  1533  is executed completely, the processor  10  can obtain multiple analyzing results (i.e., multiple weights  1535 ), and the processor  10  can then adjust the four N−1th adjusted-predicted points  83  according to the multiple weight  1535  obtained from the Nth regression tree  1533 , so as to generate four final predicted points  84 . In this invention, the processor  10  will outputs these four final predicted points  84  as the four muscle feature points  3  automatically analyzed by the analyzing device  1 , and automatically marks the four muscle feature points  3  on the to-be-identified image  2 . 
     It is worth to say that the number of the regression trees is decided depending on hardware performance of the analyzing device  1 , identification accuracy required by the user, and processing time acceptable to the user, etc. In general, once the number of regression trees gets more, the locations of the final predicted points  84  will be way closer to the correct locations that the actual muscle feature points should be on the facial image of the to-be-identified image  2 . 
     By using the marking method of the present invention, the analyzing device  1  can automatically mark at least four muscle feature points on user&#39;s facial image by way of AI function, so the user may quickly and effectively be aware of his/her current facial muscle status, and determines whether the care products/cosmetics/maintenance manner currently applied is working or not. As a conclusion, the technical solution provided by the present invention is convenient and useful to the user. 
     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.