Patent Publication Number: US-10776614-B2

Title: Facial expression recognition training system and facial expression recognition training method

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application claims priority to Taiwan Application Serial Number 107104796, filed Feb. 9, 2018, which is herein incorporated by reference in its entirety. 
     BACKGROUND 
     Technical Field 
     The present disclosure relates to a training system and a training method, and more particularly to a facial expression recognition training system and a facial expression recognition training method. 
     Description of Related Art 
     With the advancement of technology, the technology of face recognition has developed into facial expression recognition, in other words, various facial expressions of facial images such as happiness, surprise, sadness, anger, disgust, amazement, grief, etc. can be recognized through the facial expression recognition training system. 
     The conventional facial expression recognition training system is trained by utilizing a large amount of marked image data through machine learning, so that this facial expression recognition training system can recognize various facial expressions of the facial image. In other words, after the facial expression recognition training system finishes training, the parameters of the facial expression recognition training system remains unvaried basically unless the marked image data is given again for training. Accordingly, if unmarked image data is input to the facial expression recognition training system, there is a considerable probability for this facial expression recognition training system to recognize incorrectly. 
     SUMMARY 
     The present disclosure is to provide a facial expression recognition training system and a facial expression recognition training method. 
     One aspect of the present disclosure is a facial expression recognition training system including a training module, a feature database, a capturing module, a recognition module and an adjusting module. The training module trains a facial expression feature capturing model according to a plurality of known facial images. The feature database stores a plurality of known facial expression features of the known facial images. The capturing module captures a first facial image, in which the facial expression feature capturing model outputs a facial expression features of the first facial image according to the first facial image. The recognition module compares the facial expression feature with the known facial expression features, and makes the facial expression feature correspond to one of the known facial expression features accordingly. The adjusting module adjusts the facial expression feature capturing model to reduce the differences between the facial expression feature and the known facial expression feature. 
     According to one embodiment of the present disclosure, the facial expression recognition training system further includes an image calibration module configured to correct the first facial image to form a second facial image, and sharpen the second facial image. The facial expression feature capturing model recognizes and outputs the facial expression feature to the recognition module according to the sharpened second facial image. The recognition compares the facial expression feature which is processed by the image calibration module with the known facial expressions. 
     According to one embodiment of the present disclosure, the image calibration module is configured to align the facial features of each of the first facial images with each other. 
     According to one embodiment of the present disclosure, the image calibration module is configured to shift the first facial image with respect to a reference plane such that a nosal tip feature point in the first facial image is aligned with a center point of the reference plane, and rotate the shifted first facial image such that a connection line between two eyes in the shifted first facial image is parallel to a horizontal line of the reference plane, so as to form the second facial image. 
     According to one embodiment of the present disclosure, the image calibration module uses Difference of Gaussian (DoG), Sobel operator or Laplace operator to sharpen the second facial image. 
     According to one embodiment of the present disclosure, the facial expression feature capturing model may include convolutional neural network or neural network. 
     Another aspect of the present disclosure is a facial expression recognition training method including: training a facial expression feature capturing model according to a plurality of known facial images; storing a plurality of known facial expression features of the known facial images; capturing a first facial image, and the facial expression feature capturing model outputting a facial expression feature of the first facial image according to the first facial image; comparing the facial expression feature with the known facial expression features, and making the facial expression feature correspond to one of the known facial expression features accordingly; adjusting the facial expression feature capturing model to reduce the difference between the facial expression feature and the known facial expression feature. 
     According to one embodiment of the present disclosure, the step of outputting the facial expression feature according to the first facial image includes: correcting the first facial image with respect to a reference plane to form a second facial image; sharpening the second facial image; recognizing the facial expression feature according to the sharpened second facial image. 
     According to one embodiment of the present disclosure, the step of correcting the first facial image with respect to the reference plane to form the second facial image includes: shifting the first facial image with respect to a reference plane such that a nosal tip feature point in the first facial image is aligned with a center point of the reference plane; rotating the shifted first facial image such that a connection line between two eyes in the shifted first facial image is parallel to a horizontal line of the reference plane, so as to form the second facial image. 
     According to one embodiment of the present disclosure, the step of sharpening the second facial image includes: using Difference of Gaussian (DoG), Sobel operator or Laplace operator to sharpen the second facial image. 
     According to one embodiment of the present disclosure, the facial expression feature capturing model may include a convolutional neural network or a neural network. 
     The present disclosure is to correspondingly adjust the parameters of the facial expression feature capturing model every time performing the facial expression feature recognition on the unmarked facial image, so that the facial expression feature capturing model is continuously trained. Accordingly, the facial expression feature recognition ability is enhanced to achieve the purpose of adaptability and personalization. 
     It is to be understood that both the foregoing general description and the following detailed description are by examples, and are intended to provide further explanation of the disclosure as claimed. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The present disclosure can be more fully understood by reading the following detailed description of the embodiment, with reference made to the accompanying drawings as follows: 
         FIG. 1  is a functional block diagram of a facial expression recognition training system according to one embodiment of the present disclosure. 
         FIG. 2  is a flowchart of a facial expression recognition training method according to one embodiment of the present disclosure. 
         FIG. 3A  is a schematic diagram of a first facial image according to one embodiment of the present disclosure. 
         FIG. 3B  is a schematic diagram of marking key points in the first facial image shown in  FIG. 3A . 
         FIG. 3C  is a schematic diagram of selecting a facial expression feature in the first facial image shown in  FIG. 3A  according to the key points shown in  FIG. 3B . 
         FIG. 4  is a functional block diagram of a facial expression recognition training system according to another embodiment of the present disclosure. 
         FIG. 5  is a flowchart of a facial expression recognition training method according to another embodiment of the present disclosure. 
         FIG. 6  is a flowchart of further steps of the step S 240  of the facial expression recognition training method shown in  FIG. 5 . 
         FIG. 7A  is a schematic diagram of a first facial image and a reference plane according to one embodiment of the present disclosure. 
         FIG. 7B  is a schematic diagram of shifting the first facial image shown in  FIG. 7A  such that a nosal tip feature point in the first facial image is aligned with a center point of the reference plane. 
         FIG. 7C  is a schematic diagram of rotating the shifted first facial image shown in  FIG. 7B  such that a connection line between two eyes in the shifted first facial image is parallel to a horizontal line of the reference plane to form a second facial image. 
         FIG. 7D  is a schematic diagram of marking key points in the second facial image shown in  FIG. 7C . 
         FIG. 7E  is a schematic diagram of selecting a facial expression feature in the second facial image shown in  FIG. 7C  according to the key points shown in  FIG. 7D . 
     
    
    
     DETAILED DESCRIPTION 
     For the embodiment below is described in detail with the accompanying drawings, embodiments are not provided to limit the scope of the present disclosure. Moreover, the operation of the described structure is not for limiting the order of implementation. Any device with equivalent functions that is produced from a structure formed by a recombination of elements is all covered by the scope of the present disclosure. Drawings are for the purpose of illustration only, and not plotted in accordance with the original size. 
     Reference is made to  FIG. 1 , which illustrates a functional block diagram of a facial expression recognition training system  100  according to one embodiment of the present disclosure. 
     The facial expression recognition training system  100  includes a training module  110 , a feature database  120 , a capturing module  130 , a recognition module  140  and an adjusting module  150 . 
     The training module  110  is configured to train a facial expression feature capturing model  111  according to plural of known facia images KFI. 
     In one embodiment, the known facial images KFI may include facial images with various facial expressions such as happiness, surprise, sadness, anger, disgust, amazement, grief, and so on. Since the known facial images KFI with different facial expressions have different known facial expression features KFEF, the training module  110  can train the known facial images KFI according to different known facial expression features KFEF, so as to obtain the corresponding facial expression feature capturing model  111 . For example, if the facial expression of the known facial image KFI is happy, the training module  110  generates the facial expression feature capturing model  111  of which the facial expression is happy according to the known facial expression feature KFEF, e.g., the mouth feature is that the corner of the mouth is raised, of the known facial image KFI. On the other hand, if the facial expression of the known facial image KFI is surprised, the training module  110  generates the facial expression feature capturing model  111  of which the facial expression is surprised according to the known facial expression feature KFEF, e.g., the mouth feature is that the mouth is opened, of the known facial image KFI. 
     In one embodiment, the facial expression feature capturing model  111  may include a convolutional neural network or a neural network. 
     The feature database  120  is configured to store the known facial expression features KFEF of the known facial images KFI, in which the known facial expression feature KFEF can be represented by a feature vector, and the known facial expression features KFEF corresponding to different facial expressions have different feature vector ranges. For example, the known facial expression feature KFEF, of which the facial expression is happy, has a first feature vector range, and the known facial expression feature KFEF, of which the facial expression is surprised, has a second feature vector range. In other words, the feature vector that falls within the first feature vector range means that the facial expression of the known facial image KFI corresponding to this feature vector is happy, and the feature vector that falls within the second feature vector range means that the facial expression of the known facial image KFI corresponding to this feature vector is surprised. 
     The capturing module  130  is configured to continuously capture plural of first facial images FI 1 , and the first facial images FI 1  may include facial images with various facial expressions, such as happiness, surprise, sadness, anger, disgust, amazement, grief and so on. 
     In one embodiment, the capturing module  130  may be a camera, a video camera, a video recorder, or the like. 
     The facial expression feature capturing model  111  may output the facial expression feature FEF 1  according to the first facial image FI 1 , in which the facial expression feature FEF 1  corresponds to the first facial image FI 1 . In other words, after the first facial image FI 1  is recognized by the facial expression feature capturing model  111 , the facial expression feature FEF 1  of the first facial image FI 1  can be outputted. For example, after the first facial images FI 1  of happiness, surprise, sadness, anger, disgust, amazement, grief, etc. are recognized by the facial expression feature capturing model  111 , the facial expression feature capturing model  111  can output the facial expression features FEF 1  of happiness, surprise, sadness, anger, disgust, amazement, anger, grief, and so on. 
     The recognition module  140  is configured to compare the facial expression feature FEF 1  with the known facial expression features KFEF, and make the facial expression feature FEF 1  correspond to one of the known facial expression features KFEF accordingly. Taking a facial expression feature FEF 1  as an example, the recognition module  140  finds the known facial expression feature KFEF of which the feature vector is closest to the feature vector of the facial expression feature FEF 1  among the known facial expression features KFEF, and evaluates a difference between the feature vector of the facial expression feature FEF 1  and the feature vector of the found known facial expression feature KFEF. Accordingly, for the remaining facial expression features FEF 1 , it also can respectively find the known facial expression features KFEF of which the feature vector are closest to their feature vectors in the same way, and evaluate a difference between the feature vector of each of the remaining facial expression features FEF 1  and that of the found known facial expression feature KFEF. Further, all evaluated differences are calculated and summed up to obtain a value. Furthermore, when the value is close to the minimum value, the facial expression feature capturing model  111  is established. 
     The adjusting module  150  is configured to adjust the facial expression feature capturing model  111  to reduce the difference between the facial expression feature FEF 1  and the known facial expression feature KFEF 1 . 
     It should be noted that the training module  110 , the feature database  120 , the capturing module  130 , the recognition module  140 , and the adjusting module  150  may be implemented by hardware, software, firmware, or the combination thereof. 
     Reference is made to  FIG. 1  and  FIG. 2 .  FIG. 2  is a flowchart of an facial expression recognition training method M 100  according to one embodiment of the present disclosure. The facial expression recognition training method M 100  shown in  FIG. 2  can be applied to the facial expression recognition training system  100  shown in  FIG. 1 . 
     In step S 110 , the training module  110  trains the facial expression feature capturing model  111  according to plural of the known facial images KFI. 
     In step S 120 , the feature database  120  stores the known facial expression features KFEF of the known facial images KFI, such as the known facial expression feature KFEF of which the facial expression is happy or surprised. 
     In step S 130 , the capturing module  130  captures the first facial image FI 1 . Specifically, reference is also made to  FIG. 3A , which illustrates a schematic diagram of the first facial image FI 1  according to one embodiment of the present disclosure. It should be noted that  FIG. 3A  only takes one first facial image FI 1  as an example. 
     As shown in  FIG. 3A , the first facial image FI 1  is exemplified by the facial image of which the facial expression is happy. 
     In step S 140 , the facial expression feature capturing model  111  may output the facial expression feature FEF 1  of the first facial image FI 1  according to the first facial image FI 1 . Specifically, reference is also made to  FIG. 3B  and  FIG. 3C .  FIG. 3B  is a schematic diagram of marking key points KP in the first facial image FI 1  shown in  FIG. 3A .  FIG. 3C  is a schematic diagram of selecting the facial expression feature FEF 1  in the first facial image FI 1  shown in  FIG. 3A  according to the key points KP shown in  FIG. 3B . 
     As shown in  FIG. 3B , by the face recognition technology, the left eyebrow, the right eyebrow, the left eye, the right eye, the mouth, the forehead, the nose, and the contour of the face in the first facial image FI 1  can be marked with the corresponding key points KP; next, since the first facial image FI 1  is oblique, it is necessary to further perform the rotation and shift calibration on the key points KP according to the nosal tip and the connection line between two eyes; finally, the elliptic regression is used to remove the background or other unnecessary features so as to cut off the face and select the first key point KP 1  among the key points KP. Specifically, in the present embodiment, the number of the first key points KP 1  is eleven, in which each of the left eyebrow and the right eyebrow has three, each of the left eye and the right eye has one, and the mouth has three. It should be noted that the number and the position of the key points KP and the first key point KP 1  shown in  FIG. 3B  are exemplified and it is not limited thereto. 
     In addition, as shown in  FIG. 3C , the first key points KP 1  corresponding to the left eyebrow, the right eyebrow, the left eye, the right eye, and the mouth are marked such that the facial expression feature capturing model  111  may select the facial expression feature FEF 1  in the first facial image FI 1  according to the first key points KP 1 , in which the facial expression feature FEF 1  includes the left eyebrow feature, the right eyebrow feature, the left eye feature, the right eye feature, and the mouth feature. 
     Furthermore, since the facial expression of the first facial image FI 1  is happy, the left eyebrow feature, the right eyebrow feature, the left eye feature, the right eye feature, and the mouth feature of the facial expression features FEF 1  should conform to the facial features of happiness, for example, the mouth feature is that the corner of the mouth is raised. On the condition that the mouth feature is the corner of the mouth being raised, it means that the position of the first key point KP 1  marked at the corner of the mouth will move upwards while comparing to the expressionless expression. 
     In step S 150 , the recognition module  140  compares the facial expression feature FEF 1  with the known facial expression features KFEF, and makes the facial expression feature FEF 1  correspond to a known facial expression feature KFEF 1  of the known facial expression features KFEF. Specifically, since the facial expression feature FEF 1  conforms to the facial expression of happiness, the recognition module  140  selects the known facial expression feature KFEF 1  that conforms to the facial feature of happiness among the known facial expression features KFEF in the feature database  120 , in which the known facial expression feature KFEF 1  and the facial expression feature FEF 1  are not exactly the same and there is the difference between them. In short, both the facial expression feature FEF 1  and the known facial expression feature KFEF 1  conform to the facial expression of happiness, but there is the slight difference between them. For example, the mouth features of the facial expression feature FEF 1  and the known facial expression feature KFEF 1  both are that the corner of the mouth is raised, but the magnitudes of the raises are not exactly the same. 
     In step S 160 , the adjusting module  150  adjusts the facial expression feature capturing model  111  to reduce the difference between the facial expression feature FEF 1  and the known facial expression feature KFEF 1 . In detail, the associated parameters of the facial expression feature capturing model  111  can be adjusted by stochastic gradient descent (SCD) to reduce the difference between the facial expression feature FEF 1  and the known facial expression feature KFEF 1 . 
     The adjusted facial expression feature capturing model  111  can perform more accurate facial expression recognition on the facial image that needs to be processed by the facial expression feature recognition, so that the facial expression feature FEF 1  captured by the adjusted facial expression feature capturing model  111  is able to be closer to the known facial expression feature KFEF 1  in the feature database  120  to achieve the purpose of adaptability. 
     Accordingly, every time the facial expression feature capturing model  111  performs the facial expression feature recognition on the unmarked facial image, the adjusting module  150  will correspondingly adjust the parameters of the facial expression feature capturing model  111  so that the facial expression feature capturing model  111  is continuously trained to enhance the facial expression feature recognition ability of the facial expression feature capturing model  111  and achieve the purpose of adaptability and personalization; moreover, it further has the effect of adjusting the parameters of the facial expression feature capturing model  111  online; furthermore, the problem of conceptual drift is also less likely to occur. 
     Reference is made to  FIG. 4 , which illustrates a functional block diagram of a facial expression recognition training system  200  according to another embodiment of the present disclosure. 
     The facial expression recognition training system  200  includes a training module  210 , a feature database  220 , a capturing module  230 , an recognition module  240 , an adjusting module  250 , and an image calibration module  260 . 
     The training module  210  is configured to train the facial expression feature capturing model  211  according to plural of known facial images KFI. 
     In one embodiment, the facial expression feature capturing model  211  can include a convolutional neural network or a neural network. 
     The feature database  220  is configured to store known facial expression features KFEF of the known facial images KFI. 
     The capturing module  230  is configured to capture the first facial image FI 1 . 
     In one embodiment, the capturing module  230  can be a camera, a video camera, a video recorder, or the like. 
     The image calibration module  260  is configured to correct and sharpen the first facial image FI 1  with respect to a reference plane to form the second facial image FI 2 . In some embodiments, the image calibration module  260  is configured to align the facial features of each facial image (e.g., the first facial image FI 1 ) with each other. 
     The facial expression feature capturing model  211  can output the facial expression feature FEF 2  of the second facial image FI 2  according to the second facial image FI 2 . In other words, after the second facial image FI 2  is recognized by the facial expression feature capturing model  211 , the facial expression feature FEF 2  of the second facial image FI 2  can be outputted. 
     The recognition module  240  is configured to compare the facial expression feature FEF 2  with the known facial expression features KFEF, and make the facial expression feature FEF 2  correspond to a known facial expression features KFEF 2  of the known facial expression features KFEF. 
     The adjusting module  250  is configured to adjust the facial expression feature capturing model  211  to reduce the difference between the facial expression feature FEF 2  of the second facial image FI 2  and the known facial expression feature KFEF 2 . 
     It should be noted that the training module  210 , the feature database  220 , the capturing module  230 , the recognition module  240 , the adjusting module  250 , and the image calibration module  260  can be implemented by hardware, software, firmware, or the combination thereof. 
     Reference is made to  FIG. 4  and  FIG. 5 .  FIG. 4  is a functional block diagram of the facial expression recognition training system  200  according to another embodiment of the present disclosure.  FIG. 5  is a flowchart of a facial expression recognition training method M 200  according to another embodiment of the present disclosure. 
     Steps S 210  to S 230  of the facial expression recognition training method M 200  are substantially similar to the steps S 110  to S 130  of the facial expression recognition training method M 100 , and thus further description will not be given herein. Only steps S 240  to step S 280  will be described below. 
     In step S 240 , the image calibration module  260  corrects the first facial image FI 1  with respect to the reference plane RS to form the second facial image FI 2 . Further, reference is also made to  FIG. 6 , which illustrates a flowchart of further steps of step S 240  of the facial expression recognition training method M 200  shown in  FIG. 5 . 
     Step S 240  includes step S 241  and step S 242 . 
     In step S 241 , the image calibration module  260  shifts the first facial image FI 1  with respect to the reference plane RS such that the nosal tip feature point NTEP in the first facial image FI 1  is aligned with the center point CP of the reference plane RS. Specifically, reference is also made to FIG.  7 A and  FIG. 7B .  FIG. 7A  is a schematic diagram of the first facial image and the reference plane RS according to one embodiment of the present disclosure.  FIG. 7B  is a schematic diagram of shifting the first facial image shown in  FIG. 7A  such that the nosal tip feature point NTEP in the first facial image FI 1  is aligned with the center point CP of the reference plane RS. 
     As shown in  FIG. 7A , the first facial image FI 1  is exemplified by the facial image of which the facial expression is happy, and the first facial image includes the nosal tip feature point NTEP and the connection line EL between two eyes, in which the nosal tip feature point NTEP is located at the tip of the nose and the connection line EL between two eyes is the line passing through both eyes. The reference plane RS is the plane overlapping the first facial image FI 1 , and the reference plane RS includes the center point CP and the horizontal line HL passing through the center point CP. 
     As shown in  FIG. 7B , the image calibration module  260  shifts the first facial image FI 1  such that the nosal tip feature point NTEP in the first facial image FI 1  is aligned with the center point CP of the reference plane RS. 
     In step S 242 , the image calibration module  260  rotates the shifted first facial image FI 1  such that the connection line EL between two eyes in the shifted first facial image FI 1  is parallel to the horizontal line HL of the reference plane RS. Specifically, reference is made to  FIG. 7C , which illustrates a schematic diagram of rotating the shifted first facial image shown in  FIG. 7B  such that the connection line EL between two eyes in the shifted first facial image is parallel to the horizontal line HL of the reference plane RS to form the second facial image FI 2 . 
     As shown in  FIG. 7C , the second facial image FI 2  is formed by shifting and rotating the first facial image FI 1 . 
     In step S 250 , the image calibration module  260  uses Difference of Gaussian (DoG), Sobel operator or Laplace operator to sharpen the second facial image FI 2 . In detail, the image calibration module  260  performs a difference operation on eight pixels surrounding each of the pixels of the second facial image IF 2  to sharpen the second facial image IF 2 . 
     In step S 260 , the facial expression feature capturing model  211  may output the facial expression feature FEF 2  according to the sharpened second facial image FI 2 . Specifically, reference is made to  FIG. 7D  and  FIG. 7E .  FIG. 7D  is a schematic diagram of marking key points KP in the second facial image FI 2  (especially the sharpened second facial image FI 2 ) shown in  FIG. 7C , and  FIG. 7E  is a schematic diagram of selecting the facial expression feature FEF 2  in the second facial image IF 2  shown in  FIG. 7C  according to the key points KP shown in  FIG. 7D . 
     As shown in  FIG. 7D , the facial expression feature capturing model  211  can mark plural of key points KP in the second facial image FI 2  according to the contour of the face and the characteristics of the facial features, for example, the left eyebrow, the right eyebrow, the left eye, the right eye, the mouth, the forehead, the nose, and the contour of face in the second facial image FI 2  are marked with the corresponding key points KP, and the elliptic regression is used to select the first key points KP 1  among the key points KP. Further, in the present embodiment, the number of the first key points KP 1  is eleven, in which each of the left eyebrow and the right eyebrow has three, each of the left eye and the right eye has one, and the mouth has three. It should be noted that the number and the position of the key points KP and the first key points KP 1  shown in  FIG. 7D  are exemplified, and it is not limited thereto. 
     In addition, as shown in  FIG. 7D , the first key points KP 1  corresponding to the left eyebrow, the right eyebrow, the left eye, the right eye, and the mouth are marked to select the facial expression feature FEF 2  in the second facial image FI 2 , in which the facial expression feature FEF 2  includes the left eyebrow feature, the right eyebrow feature, the left eye feature, the right eye feature, and the mouth feature. 
     Furthermore, since the facial expression of the second facial image FI 2  is happy, the left eyebrow feature, the right eyebrow feature, the left eye feature, the right eye feature, and the mouth feature of the facial expression feature FEF 2  should conform to the facial features of happiness, for example, the mouth feature is that the corner of the mouth is raised. On the condition that the mouth feature is the corner of the mouth being raised, it means that the position of the first key point KP 1  marked at the corner of the mouth will move upwards while comparing to the expressionless expression. 
     In step S 270 , the recognition module  240  compares the facial expression feature FEF 2  with the known facial expression features KFEF, and makes the facial expression feature FEF 2  correspond to a known facial expression feature KFEF 2  of the known facial expression features KFEF. Specifically, since the facial expression feature FEF 2  conforms to the facial expression of happiness, the recognition module  240  selects the known facial expression feature KFEF 2  that conforms to the facial feature of happiness among the known facial expression features KFEF in the feature database  220 , in which the known facial expression feature KFEF 2  and the facial expression feature FEF 2  are not exactly the same and there is the difference between them. In short, both the facial expression feature FEF 2  and the known facial expression feature KFEF 2  conform to the facial expression of happiness, but there is the slight difference between them. For example, the mouth features of the facial expression feature FEF 2  and the known facial expression feature KFEF 2  both are the corner of the mouth being raised, but the magnitudes of the raises are not exactly the same. 
     In step S 280 , the adjusting module  250  adjusts the facial expression feature capturing model  211  to reduce the difference between the facial expression feature FEF 2  and the known facial expression feature KFEF 2 . In detail, the associated parameters of the facial expression feature capturing model  211  can be adjusted by stochastic gradient descent (SCD) to reduce the difference between the facial expression feature FEF 2  and the known facial expression feature KFEF 2 . 
     The adjusted facial expression feature capturing model  211  can perform more accurate facial expression recognition on the facial image that needs to be processed by the facial expression feature recognition, so that the facial expression feature FEF 2  captured by the adjusted facial expression feature capturing model  211  is able to be closer to the known facial expression feature KFEF 2  in the feature database  220  to achieve the purpose of adaptability. 
     Accordingly, every time the facial expression feature capturing model  211  performs the facial expression feature recognition on the unmarked facial image, the adjusting module  250  will correspondingly adjust the parameters of the facial expression feature capturing model  211  so that the facial expression feature capturing model  211  is continuously trained to enhance the facial expression feature recognition ability of the facial expression feature capturing model  211  and achieve the purpose of adaptability and personalization; moreover, it further has the effect of adjusting the parameters of the facial expression feature capturing model  211  online; furthermore, the problem of conceptual drift is also less likely to occur. 
     As mentioned above, by the training module, the feature database, the capturing module, the recognition module, and the adjusting module, the facial expression recognition training system of the present disclosure enables the facial expression feature capturing model to be continuously trained to enhance the facial expression feature recognition ability of the facial expression feature capturing model and achieve the purpose of adaptability and personalization; moreover, it further has the effect of adjusting the parameters of the facial expression feature capturing model online; furthermore, the problem of conceptual drift is also less likely to occur. 
     It will be apparent to those skilled in the art that various modifications and variations can be made to the structure of the present disclosure without departing from the scope or spirit of the present disclosure. In view of the foregoing, it is intended that the present disclosure cover modifications and variations of this present disclosure provided they fall within the scope of the following claims.