WO2019095571A1

WO2019095571A1 - Human-figure emotion analysis method, apparatus, and storage medium

Info

Publication number: WO2019095571A1
Application number: PCT/CN2018/076168
Authority: WO
Inventors: 陈林
Original assignee: 平安科技（深圳）有限公司
Priority date: 2017-11-15
Filing date: 2018-02-10
Publication date: 2019-05-23
Also published as: CN107862292B; CN107862292A

Abstract

Provided are a human-figure emotion analysis method, analysis program, electronic apparatus, and storage medium, said method comprising: obtaining a real-time image captured by a camera apparatus, and using a face recognition algorithm to extract a real-time face image from said real-time image (S10); entering the real-time face image into a predetermined AU classifier to obtain a probability of each AU recognized from the real-time face image (S20); composing the probabilities of all AUs in the real-time face image into feature vectors of the real-time face image (S30); inputting said feature vectors into a predetermined emotion classifier to obtain the probability of each emotion recognized from the real-time face image, and taking the emotion having the greatest probability to be the emotion recognized from the real-time face image (S40). After using the method to identify the AU features and probabilities in a real-time face image, a human-figure emotion in the real-time face image is recognized according to each AU feature and probability, improving the efficiency of human-figure emotion recognition.

Description

Character emotion analysis method, device and storage medium

Priority claim

This application is based on the priority of the Chinese Patent Application entitled "People's Emotional Analysis Method, Apparatus and Storage Medium" filed on November 15, 2017, with the application number CN201711126632.2 submitted on November 15, 2017. The overall content of the Chinese patent application It is incorporated herein by reference.

Technical field

The present application relates to the field of computer vision processing technologies, and in particular, to a character emotion analysis method, a program, an electronic device, and a computer readable storage medium.

Background technique

Face emotion recognition is an important part of human-computer interaction and emotion calculation research, involving psychology, sociology, anthropology, life sciences, cognitive science, computer science and other research fields. It is very intelligent and intelligent for human-computer interaction. significance.

Internationally renowned psychologist Paul Ekman and research partner W.V.Friesen have conducted in-depth research to visualize the correspondence between different facial muscle movements and different expressions through observation and biofeedback. FACS is a "facial expression coding system" created in 1976 after years of research. According to the anatomical features of the face, it can be divided into several independent and interrelated motion units (AU). The characteristics of the movement and the main areas it controls can reflect facial expressions.

At present, it is common to identify AU features in face images and combine AU features to determine facial emotions. However, this method does not take into account the weight of each AU. For example, some people have some natural eyebrows, but AU After detection, it is directly counted as a member of the AU combination to participate in emotional judgment, so that there may be emotional misjudgment, resulting in low accuracy of emotional recognition.

Summary of the invention

The present application provides a character emotion analysis method, a program, an electronic device, and a computer readable storage medium, the main purpose of which is to identify a real-time face according to each AU feature and probability by identifying an AU feature and probability in a real-time face image. The emotions of the characters in the image effectively improve the efficiency of the character's emotion recognition.

To achieve the above objective, the present application provides an electronic device, including: a memory, a processor, and an imaging device, wherein the memory includes a character emotion analysis program, and the character emotion analysis program is implemented by the processor to implement the following step:

Obtaining a real-time image captured by the camera device, and extracting a real-time facial image from the real-time image by using a face recognition algorithm;

Inputting the real-time facial image into a predetermined AU classifier to obtain a probability of each AU identified from the real-time facial image;

Generating the probability of all AUs in the real-time facial image into feature vectors of the real-time facial image; and

The feature vector is input to a predetermined emotion classifier to obtain a probability of identifying each emotion from the real-time face image, and the emotion with the highest probability is taken as the emotion recognized from the real-time face image.

In addition, in order to achieve the above object, the present application further provides a method for character emotion analysis, the method comprising:

In addition, in order to achieve the above object, the present application further provides a computer readable storage medium including a character emotion analysis program, when the character emotion analysis program is executed by a processor, implementing the above Any step in the character's sentiment analysis method.

In addition, in order to achieve the above object, the present application further provides a character emotion analysis program, which includes: an acquisition module, an AU recognition module, a feature extraction module, and an emotion recognition module, and when the character emotion analysis program is executed by a processor, Any of the steps in the character emotion analysis method described above.

The character emotion analysis method, the program, the electronic device and the computer readable storage medium proposed by the present application, by identifying a real-time facial image from a real-time image, extracting each AU feature in the real-time facial image through an AU classifier, The probabilities of the AU features are combined into a feature vector, and the feature vector is input to the emotion classifier to recognize the probability of each emotion existing in the real-time facial image, and the emotion with the highest probability is taken as the emotion in the real-time image. By combining the AU classifier and the emotion classifier to identify emotions in real-time facial images, the recognition efficiency of the characters' emotions is effectively improved.

DRAWINGS

1 is a schematic diagram of an application environment of a preferred embodiment of an applicant's sentiment analysis method;

2 is a block diagram showing a preferred embodiment of the character emotion analysis program of FIG. 1;

3 is a flow chart of a preferred embodiment of the applicant's sentiment analysis method.

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings.

Detailed ways

It is understood that the specific embodiments described herein are merely illustrative of the application and are not intended to be limiting.

The present application provides a character emotion analysis method applied to an electronic device 1. Referring to FIG. 1 , it is a schematic diagram of an application environment of a preferred embodiment of the applicant's emotion analysis method.

In this embodiment, the electronic device 1 may be a terminal device having a computing function, such as a server, a smart phone, a tablet computer, a portable computer, or a desktop computer.

The electronic device 1 includes a processor 12, a memory 11, an imaging device 13, a network interface 14, and a communication bus 15.

The memory 11 includes at least one type of readable storage medium. The at least one type of readable storage medium may be a non-volatile storage medium such as a flash memory, a hard disk, a multimedia card, a card type memory 11, or the like. In some embodiments, the readable storage medium may be an internal storage unit of the electronic device 1, such as a hard disk of the electronic device 1. In other embodiments, the readable storage medium may also be an external memory 11 of the electronic device 1, such as a plug-in hard disk equipped on the electronic device 1, a smart memory card (SMC). , Secure Digital (SD) card, Flash Card, etc.

In this embodiment, the readable storage medium of the memory 11 is generally used to store a character emotion analysis program 10 installed on the electronic device 1, a face image sample library, and a pre-trained AU classifier and an emotion classifier. Wait. The memory 11 can also be used to temporarily store data that has been output or is about to be output.

The processor 12, in some embodiments, may be a Central Processing Unit (CPU), microprocessor or other data processing chip for running program code or processing data stored in the memory 11, such as performing character sentiment analysis. Program 10 and so on.

The imaging device 13 may be part of the electronic device 1 or may be independent of the electronic device 1. In some embodiments, the electronic device 1 is a terminal device having a camera such as a smartphone, a tablet computer, a portable computer, etc., and the camera device 13 is a camera of the electronic device 1. In other embodiments, the electronic device 1 may be a server, and the camera device 13 is connected to the electronic device 1 via a network, for example, the camera device 13 is installed in a specific place, such as an office. And monitoring the area, real-time image is taken in real time for the target entering the specific place, and the captured real-time image is transmitted to the processor 12 through the network.

The network interface 14 can optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), and is typically used to establish a communication connection between the electronic device 1 and other electronic devices.

Communication bus 15 is used to implement connection communication between these components.

Figure 1 shows only the electronic device 1 with components 11-15, but it should be understood that not all illustrated components may be implemented, and more or fewer components may be implemented instead.

Optionally, the electronic device 1 may further include a user interface, and the user interface may include an input unit such as a keyboard, a voice input device such as a microphone, a device with a voice recognition function, a voice output device such as an audio, a headphone, and the like. Optionally, the user interface may also include a standard wired interface and a wireless interface.

Optionally, the electronic device 1 may further include a display, which may also be referred to as a display screen or a display unit. In some embodiments, it may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an Organic Light-Emitting Diode (OLED) touch sensor. The display is used to display information processed in the electronic device 1 and a user interface for displaying visualizations.

Optionally, the electronic device 1 further comprises a touch sensor. The area provided by the touch sensor for the user to perform a touch operation is referred to as a touch area. Further, the touch sensor described herein may be a resistive touch sensor, a capacitive touch sensor, or the like. Moreover, the touch sensor includes not only a contact type touch sensor but also a proximity type touch sensor or the like. Furthermore, the touch sensor may be a single sensor or a plurality of sensors arranged, for example, in an array.

In addition, the area of the display of the electronic device 1 may be the same as or different from the area of the touch sensor. Optionally, a display is stacked with the touch sensor to form a touch display. The device detects a user-triggered touch operation based on a touch screen display.

Optionally, the electronic device 1 may further include a radio frequency (RF) circuit, a sensor, an audio circuit, and the like, and details are not described herein.

In the apparatus embodiment shown in FIG. 1, an operating system and a character emotion analysis program 10 may be included in the memory 11 as a computer storage medium; when the processor 12 executes the character emotion analysis program 10 stored in the memory 11, the following is realized as follows step:

Obtaining a real-time image captured by the camera device 13 and extracting a real-time face image from the real-time image by using a face recognition algorithm;

According to Paul Ekman's Facial Action Coding System (FACS), humans have a total of 39 major Action Units (AUs). Each AU is a small group of muscle contraction codes for the face. For example, AU1- raises the inner corner of the eyebrow, AU2- raises the outer corner of the eyebrow, AU9-wrinkles the nose, AU22- tightens the lips and turns outwards.

When the camera 13 captures a real-time image, the camera 13 transmits the real-time image to the processor 12. After the processor 12 receives the real-time image, the image is first acquired to create a grayscale image of the same size. Converting the acquired color image into a grayscale image and creating a memory space; equalizing the grayscale image histogram, reducing the amount of grayscale image information, speeding up the detection speed, and then loading the training library to detect the person in the image Face, and return an object containing face information, obtain the data of the location of the face, and record the number; finally obtain the area of the avatar and save it, thus completing a real-time facial image extraction process.

Specifically, the face recognition algorithm for extracting the real-time facial image from the real-time image may also be: a geometric feature-based method, a local feature analysis method, a feature face method, an elastic model-based method, a neural network method, and the like.

Next, a real-time facial image extracted from the real-time image using a face recognition algorithm is input to a predetermined AU classifier, each AU is identified from the real-time facial image, and each of the real-time facial images is included The probability of the AU (in the range of 0-1), wherein the training steps of the predetermined AU classifier include:

In the sample preparation phase of each AU, a certain number of face images are prepared, and from a large number of face images, a face image containing the AU is found as a positive sample image of the AU, and a negative sample is prepared for each AU. Image, obtaining a positive sample image and a negative sample image of each AU to form a first sample set; image regions corresponding to different AUs may be the same, for example, AU1, AU2 are related to the face image including eyebrows, eyes, and The area of the forehead, AU9, AU22, relates to the nose and lip areas in the face image. The area of the image that does not contain the AU can be used as the negative sample image of the AU.

A first scale (for example, 60%) of sample images are randomly extracted from each of the AU positive/negative sample images as a training set, and a second proportional sample image is extracted from the remaining sample images of the AU as a verification set. For example, 50%, that is, 20% of all sample images of the AU are extracted as a verification set, and the convolutional neural network (CNN) is trained by using the training set of each AU to obtain the AU classifier; in order to ensure the AU classifier Accuracy, the accuracy of the AU classifier needs to be verified, and the accuracy of the trained AU classifier is verified by the verification set. If the accuracy rate is greater than or equal to the preset accuracy rate (for example, 90%), the training is performed. End, or, if the accuracy is less than the preset accuracy, increase the number of sample pictures in the sample set and re-execute the above training steps.

It should be noted that the training step of the predetermined AU classifier further includes: performing preprocessing such as scaling, cropping, flipping, and/or twisting on the sample image in the first sample set, using the preprocessed The sample image trains the convolutional neural network to effectively improve the authenticity and accuracy of the model training.

Assuming that the real-time facial image is detected and output by the AU classifier, the probability of identifying each AU (for example, 39) from the real-time facial image is P1, P2, P3, ..., P39, respectively, and each AU The probability consists of a feature vector V1, V1 = [P1, P2, P3, ..., P39] as a feature vector of the real-time facial image.

The feature vector V1 is then input to a predetermined emotion classifier to identify each of the emotions that may exist from the real-time face image, as well as the probability of each emotion (ie, the likelihood of being present). The training steps of the predetermined emotion classifier include:

By using the sample image in the first sample set, the sample image is input into the AU classifier, and the probability of identifying each AU from the sample image is obtained, and the probability of identifying each AU from each sample image is combined into one feature. At the same time, according to the emotions presented by each sample image, the sample images are classified and labeled, that is, an emotion label (for example, “happy”) is assigned, and a second sample set including the feature vector and the emotion label is obtained; from the second sample set. Randomly extracting a sample image of a first scale (for example, 60%) as a training set, and extracting a second scale sample image from the remaining sample images as a verification set, for example, 50%, that is, extracting 20% of the sample image in the second sample set As a verification set, the training set is trained by using a naive Bayes algorithm to obtain the emotion classifier; in order to ensure the accuracy of the emotion classifier, the accuracy of the emotion classifier needs to be verified, and the verification set is utilized. Verifying the accuracy of the trained emotion classifier, if the accuracy rate is greater than or equal to the preset accuracy rate (eg, 90%), the training ends Or, if accuracy is less than a preset accuracy, increase the number of sample image sample set of training and re-execute the above steps.

It should be noted that the Naive Bayes algorithm is divided into three types, namely GaussianNB, MultinomialNB and BernoulliNB. Among them, GaussianNB is a simple Bayesian with Gaussian distribution a priori, MultinomialNB is a simple Bayesian with a priori polynomial distribution, and BernoulliNB is a simple Bayesian with a priori Bernoulli distribution. The classification scenarios applicable to these three categories are different, and GaussianNB is used for classification problems. MultinomialNB and BernoulliNB are used for discrete value models. Because we are a classification problem, we chose GaussianNB.

Among them, GaussianNB assumes that the prior probability of the feature is a normal distribution, so there is the following formula:

Where _{k k} is the kth class of Y, μ _k and

For values that need to be estimated from the training set. GaussianNB will find μ _k and based on the training set.

μ _k is the average of all X _j in the sample class C _k .

Is the variance of all X _j in the sample class C _k .

Assuming that the feature vector V1 of the real-time facial image is input to the emotion classifier, there are various emotions recognized from the real-time facial image, and the probability of each emotion is recognized (the range of values is: 0-1) ) different, for example, happy: 0.6, surprised: 0.3, sad: 0.1.

In the output result of the above-described emotion classifier, when the probability of recognizing "happy" from the real-time face image is the largest, it is determined that the emotion recognized from the real-time face image is "happy".

It should be noted that the parameters that need to be preset in the first probability, the second probability, the preset accuracy, and the like described in the foregoing embodiments may be adjusted according to user requirements.

The electronic device 1 proposed in the above embodiment extracts the real-time facial image from the real-time image, extracts each AU feature in the real-time facial image through the AU classifier, and combines the probabilities of the AU features into feature vectors to feature The vector input emotion classifier identifies the probability of each emotion present in the real-time facial image, and takes the emotion with the highest probability as the emotion in the real-time image. By combining the AU classifier and the emotion classifier to identify emotions in real-time facial images, the recognition efficiency of the characters' emotions is effectively improved.

In particular, the character sentiment analysis program 10 can be partitioned into one or more modules, one or more modules being stored in the memory 11 and executed by the processor 12 to complete the application. A module as referred to in this application refers to a series of computer program instructions that are capable of performing a particular function. Referring to FIG. 2, it is a functional block diagram of a preferred embodiment of the character emotion analysis program 10 of FIG. The character emotion analysis program 10 can be divided into: an acquisition module 110, an AU recognition module 120, a feature extraction module 130, and an emotion recognition module 140. The functions or operational steps implemented by the modules 110-140 are similar to the above, and are not described in detail herein, by way of example, for example:

The acquiring module 110 is configured to acquire a real-time image captured by the camera device 13 and extract a real-time facial image from the real-time image by using a face recognition algorithm;

The AU identification module 120 is configured to input the real-time facial image into a predetermined AU classifier to obtain a probability of each AU identified from the real-time facial image;

a feature extraction module 130, configured to form a probability vector of all AUs in the real-time facial image into a feature vector of the real-time facial image; and

The emotion recognition module 140 is configured to input the feature vector into a predetermined emotion classifier, obtain a probability of identifying each emotion from the real-time face image, and take the emotion with the highest probability as the recognition from the real-time face image Emotions.

In addition, the present application also provides a method for character emotion analysis. Referring to Figure 3, there is shown a flow chart of a preferred embodiment of the applicant's sentiment analysis method. The method can be performed by a device that can be implemented by software and/or hardware.

In the embodiment, the character emotion analysis method includes: step S10 - step S40.

Step S10: Acquire a real-time image captured by the camera device, and extract a real-time face image from the real-time image by using a face recognition algorithm.

When the camera captures a real-time image, the camera sends the real-time image to the processor. When the processor receives the real-time image, the image is first acquired to create a grayscale image of the same size; Color image, converted into gray image, and create a memory space; equalize the gray image histogram, reduce the amount of gray image information, speed up the detection, then load the training library, detect the face in the picture, and return An object containing face information, obtains the data of the location of the face, and records the number; finally obtains the area of the avatar and saves it, thus completing a real-time facial image extraction process.

In step S20, the real-time facial image is input into a predetermined AU classifier to obtain a probability of each AU identified from the real-time facial image.

A first scale (for example, 60%) of sample images are randomly extracted from each of the AU positive/negative sample images as a training set, and a second proportional sample image is extracted from the remaining sample images of the AU as a verification set. For example, 50%, that is, 20% of all sample images of the AU are extracted as a verification set, and the convolutional neural network is trained by using the training set of each AU to obtain the AU classifier; in order to ensure the accuracy of the AU classifier, The accuracy of the AU classifier needs to be verified, and the accuracy of the trained AU classifier is verified by the verification set. If the accuracy rate is greater than or equal to the preset accuracy rate (for example, 90%), the training ends, or If the accuracy is less than the preset accuracy, increase the number of sample pictures in the sample set and re-execute the above training steps.

In step S30, the probability of all AUs in the real-time facial image is composed into the feature vector of the real-time facial image.

Step S40: Input the feature vector into a predetermined emotion classifier to obtain a probability of identifying each emotion from the real-time face image, and take the emotion with the highest probability as the emotion recognized from the real-time face image.

The feature vector V1 is input to a predetermined emotion classifier to identify each emotion that may exist from the real-time face image, and the probability of each emotion (ie, the possibility of existence). The training steps of the predetermined emotion classifier include:

In this embodiment, by using the sample image in the first sample set, the sample image is input into the AU classifier, and the probability of identifying each AU from the sample image is obtained, and each sample image is identified from each sample image. The probability of AU constitutes a feature vector; at the same time, according to the emotion presented by each sample image, the sample image is classified and labeled, that is, an emotion tag (for example, “happy”) is assigned, and a second sample set including the feature vector and the emotion tag is obtained. And randomly extracting a first proportion (for example, 60%) of the sample image from the second sample set as a training set, and extracting a second proportional sample image from the remaining sample image as a verification set, for example, 50%, that is, extracting the second sample The 20% sample image is collected as a verification set, and the training set is trained by the Naive Bayes algorithm to obtain the emotion classifier. In order to ensure the accuracy of the emotion classifier, the accuracy of the emotion classifier needs to be verified. Using the verification set to verify the accuracy of the trained emotion classifier, if the accuracy rate is greater than or equal to the preset accuracy rate (eg, 90) ), The end of the training, or, if less than the preset accuracy accuracy, increase the number of sample image sample set of training and re-execute the above steps.

Where _{k k} is the kth class of Y, μ _k and

μ _k is the average of all X _j in the sample class C _k .

Is the variance of all X _j in the sample class C _k .

It should be noted that the parameters that need to be preset in the first probability, the second probability, the preset accuracy, and the like described in the foregoing embodiments may be adjusted according to user requirements. The character emotion analysis method proposed by the above embodiment, by identifying a real-time facial image from a real-time image, extracting each AU feature in the real-time facial image by an AU classifier, and combining the probabilities of the AU features into a feature vector, The feature vector input emotion classifier recognizes the probability of each emotion existing in the real-time face image, and takes the emotion with the highest probability as the emotion in the real-time image. By combining the AU classifier and the emotion classifier to identify emotions in real-time facial images, the recognition efficiency of the characters' emotions is effectively improved.

In addition, the embodiment of the present application further provides a computer readable storage medium, where the character readable analysis program includes a character emotion analysis program, and when the character emotion analysis program is executed by the processor, the following operations are implemented:

Preferably, the training step of the predetermined AU classifier comprises:

Preparing a first sample set containing a certain number of face sample images, respectively extracting each AU matched image region as a positive sample image of the AU from the face sample image, and preparing a negative sample image for each AU;

The positive/negative sample images of each AU are divided into a training set of a first ratio and a verification set of a second ratio;

Using the training set to train a convolutional neural network to obtain the AU classifier; and

Using the verification set to verify the accuracy of the trained AU classifier, if the accuracy rate is greater than or equal to the preset accuracy rate, the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the sample size and re-execute Training steps.

Preferably, the training step of the predetermined AU classifier further includes:

The pre-processing operations are performed on the sample images in the first sample set, including: scaling, cropping, flipping, and/or warping.

Preferably, the training step of the predetermined emotion classifier comprises:

Assigning an emotion tag to each sample image according to the emotion of each sample image in the first sample set, and obtaining a second sample set including the feature vector and the emotion tag;

Dividing the sample image in the second sample set into a training set of a first ratio and a verification set of a second ratio;

Training the training set using a naive Bayesian algorithm to obtain the emotion classifier; and

Using the verification set to verify the accuracy of the trained emotion classifier, if the accuracy rate is greater than or equal to the preset accuracy rate, the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the number of samples in the sample set And re-execute the above training steps.

Preferably, the naive Bayesian algorithm is a naive Bayesian algorithm that is a priori Gaussian distribution.

Preferably, the face recognition algorithm may be a geometric feature based method, a local feature analysis method, a feature face method, an elastic model based method, and a neural network method.

The specific implementation manner of the computer readable storage medium of the present application is substantially the same as the specific embodiment of the character emotion analysis method and the electronic device, and details are not described herein again.

It is to be understood that the term "comprises", "comprising", or any other variants thereof, is intended to encompass a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a series of elements includes those elements. It also includes other elements not explicitly listed, or elements that are inherent to such a process, device, item, or method. An element that is defined by the phrase "comprising a ..." does not exclude the presence of additional equivalent elements in the process, the device, the item, or the method that comprises the element.

The serial numbers of the embodiments of the present application are merely for the description, and do not represent the advantages and disadvantages of the embodiments. Through the description of the above embodiments, those skilled in the art can clearly understand that the foregoing embodiment method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be through hardware, but in many cases, the former is better. Implementation. Based on such understanding, the technical solution of the present application, which is essential or contributes to the prior art, may be embodied in the form of a software product stored in a storage medium (such as ROM/RAM as described above). , a disk, an optical disk, including a number of instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the methods described in the various embodiments of the present application.

The above is only a preferred embodiment of the present application, and is not intended to limit the scope of the patent application, and the equivalent structure or equivalent process transformations made by the specification and the drawings of the present application, or directly or indirectly applied to other related technical fields. The same is included in the scope of patent protection of this application.

Claims

A character emotion analysis method is applied to an electronic device, characterized in that the method comprises:

Obtaining a real-time image captured by the camera device, and extracting a real-time facial image from the real-time image by using a face recognition algorithm;

Inputting the real-time facial image into a predetermined AU classifier to obtain a probability of each AU identified from the real-time facial image;

Generating the probability of all AUs in the real-time facial image into feature vectors of the real-time facial image; and

The feature vector is input to a predetermined emotion classifier to obtain a probability of identifying each emotion from the real-time face image, and the emotion with the highest probability is taken as the emotion recognized from the real-time face image.
The character emotion analysis method according to claim 1, wherein the training step of the predetermined AU classifier comprises:

Preparing a first sample set containing a certain number of face sample images, respectively extracting each AU matched image region as a positive sample image of the AU from the face sample image, and preparing a negative sample image for each AU;

The positive/negative sample images of each AU are divided into a training set of a first ratio and a verification set of a second ratio;

Using the training set to train a convolutional neural network to obtain the AU classifier; and

Using the verification set to verify the accuracy of the trained AU classifier, if the accuracy rate is greater than or equal to the preset accuracy rate, the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the sample size and re-execute Training steps.
The character emotion analysis method according to claim 2, wherein the training step of the predetermined AU classifier further comprises:

The pre-processing operations are performed on the sample images in the first sample set, including: scaling, cropping, flipping, and/or warping.
The character emotion analysis method according to claim 1 or 3, wherein the training step of the predetermined emotion classifier comprises:

Assigning an emotion tag to each sample image according to the emotion of each sample image in the first sample set, and obtaining a second sample set including the feature vector and the emotion tag;

Dividing the sample images in the second sample set into a training set of a first ratio and a verification set of a second ratio;

Training the training set using a naive Bayesian algorithm to obtain the emotion classifier; and

Using the verification set to verify the accuracy of the trained emotion classifier, if the accuracy rate is greater than or equal to the preset accuracy rate, the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the number of samples in the sample set And re-execute the above training steps.
The character emotion analysis method according to claim 4, wherein the naive Bayesian algorithm is a naive Bayesian algorithm that is a priori Gaussian distribution.
The character emotion analysis method according to claim 1, wherein the face recognition algorithm is a geometric feature based method, a local feature analysis method, a feature face method, an elastic model based method, and a neural network method.
An electronic device, comprising: a memory, a processor and a camera device, wherein the memory includes a character emotion analysis program, and when the character emotion analysis program is executed by the processor, the following steps are implemented:

Obtaining a real-time image captured by the camera device, and extracting a real-time facial image from the real-time image by using a face recognition algorithm;

Inputting the real-time facial image into a predetermined AU classifier to obtain a probability of each AU identified from the real-time facial image;

Generating the probability of all AUs in the real-time facial image into feature vectors of the real-time facial image; and

The feature vector is input to a predetermined emotion classifier to obtain a probability of identifying each emotion from the real-time face image, and the emotion with the highest probability is taken as the emotion recognized from the real-time face image.
The electronic device according to claim 7, wherein the training step of the predetermined AU classifier comprises:

Preparing a first sample set containing a certain number of face sample images, respectively extracting each AU matched image region as a positive sample image of the AU from the face sample image, and preparing a negative sample image for each AU;

The positive/negative sample images of each AU are divided into a training set of a first ratio and a verification set of a second ratio;

Using the training set to train a convolutional neural network to obtain the AU classifier; and

Using the verification set to verify the accuracy of the trained AU classifier, if the accuracy rate is greater than or equal to the preset accuracy rate, the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the sample size and re-execute Training steps.
The electronic device according to claim 8, wherein the training step of the predetermined AU classifier further comprises:

The pre-processing operations are performed on the sample images in the first sample set, including: scaling, cropping, flipping, and/or warping.
The electronic device according to claim 7, wherein the training step of the predetermined emotion classifier comprises:

Assigning an emotion tag to each sample image according to the emotion of each sample image in the first sample set, and obtaining a second sample set including the feature vector and the emotion tag;

Dividing the sample images in the second sample set into a training set of a first ratio and a verification set of a second ratio;

Training the training set using a naive Bayesian algorithm to obtain the emotion classifier; and

Using the verification set to verify the accuracy of the trained emotion classifier, if the accuracy rate is greater than or equal to the preset accuracy rate, the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the number of samples in the sample set And re-execute the above training steps.
The electronic device according to claim 10, wherein the naive Bayes algorithm is a naive Bayesian algorithm that is a priori Gaussian distribution.
The electronic device according to claim 7, wherein the face recognition algorithm is a geometric feature based method, a local feature analysis method, a feature face method, an elastic model based method, and a neural network method. .
A computer readable storage medium, comprising: a character emotion analysis program, wherein when the character emotion analysis program is executed by a processor, the following steps are implemented:

Obtaining a real-time image captured by the camera device, and extracting a real-time facial image from the real-time image by using a face recognition algorithm;

Inputting the real-time facial image into a predetermined AU classifier to obtain a probability of each AU identified from the real-time facial image;

Generating the probability of all AUs in the real-time facial image into feature vectors of the real-time facial image; and

The feature vector is input to a predetermined emotion classifier to obtain a probability of identifying each emotion from the real-time face image, and the emotion with the highest probability is taken as the emotion recognized from the real-time face image.
The computer readable storage medium of claim 13, wherein the training step of the predetermined AU classifier comprises:

Preparing a first sample set containing a certain number of face sample images, respectively extracting each AU matched image region as a positive sample image of the AU from the face sample image, and preparing a negative sample image for each AU;

The positive/negative sample images of each AU are divided into a training set of a first ratio and a verification set of a second ratio;

Using the training set to train a convolutional neural network to obtain the AU classifier; and

Using the verification set to verify the accuracy of the trained AU classifier, if the accuracy rate is greater than or equal to the preset accuracy rate, the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the sample size and re-execute Training steps.
The computer readable storage medium according to claim 14, wherein the training step of the predetermined AU classifier further comprises:

The pre-processing operations are performed on the sample images in the first sample set, including: scaling, cropping, flipping, and/or warping.
The computer readable storage medium of claim 13, wherein the training step of the predetermined emotion classifier comprises:

Assigning an emotion tag to each sample image according to the emotion of each sample image in the first sample set, and obtaining a second sample set including the feature vector and the emotion tag;

Dividing the sample images in the second sample set into a training set of a first ratio and a verification set of a second ratio;

Training the training set using a naive Bayesian algorithm to obtain the emotion classifier; and

Using the verification set to verify the accuracy of the trained emotion classifier, if the accuracy rate is greater than or equal to the preset accuracy rate, the training ends, or if the accuracy rate is less than the preset accuracy rate, increase the number of samples in the sample set And re-execute the above training steps.
The computer readable storage medium according to claim 16, wherein the naive Bayesian algorithm is a naive Bayesian algorithm that is a priori Gaussian distribution.
The computer readable storage medium according to claim 13, wherein the face recognition algorithm is a geometric feature based method, a local feature analysis method, a feature face method, an elasticity model based method, and a neural network method.
A character emotion analysis program is characterized in that the program comprises: an acquisition module, an AU recognition module, a feature extraction module, and an emotion recognition module.
The character emotion analysis program according to claim 19, wherein the step of the character emotion analysis method according to any one of claims 1 to 6 is implemented when the character emotion analysis program is executed by the processor.