CN111128368B - Automatic autism spectrum disorder detection method and device based on video expression behavior analysis - Google Patents
Automatic autism spectrum disorder detection method and device based on video expression behavior analysis Download PDFInfo
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Abstract
The invention discloses an autism spectrum disorder automatic detection method and device based on video expression behavior analysis, which comprises the following steps: s1, video acquisition: acquiring video data of a tested infant and a caregiver in an interaction process during a face-still period under a face-still paradigm; s2, face preprocessing: detecting the face position of the tested infant in the video, cutting out a face area, and correcting and normalizing the faces with different postures; s3, expression behavior index feature extraction: extracting texture features from the preprocessed face image, classifying the input texture features to obtain a face motion unit prediction result, taking the result as an input signal, counting the signal in a time dimension according to the window length to obtain expression segmentation statistical features, and further taking the expression segmentation statistical features as expression behavior index features; s4, risk prediction of high-risk autism spectrum disorder: and predicting whether the infant is the high-risk autism spectrum disorder or not by using the expression behavior characteristics.
Description
Technical Field
The invention relates to an automatic screening system for high-risk autism spectrum disorder within 24 months, in particular to an automatic autism spectrum disorder detection device based on video expression behavior analysis.
Background
Autism spectrum disorder is a neurodevelopmental disorder characterized by social disturbances, language and non-language communication disorders, and restrictive and repetitive behaviors. On a global scale, it is estimated that 2480 million people suffer from autism. Currently, physicians can determine whether a child is suffering from autism spectrum disorder until the child is 3-4 years old. However, for early intervention in infants with autism spectrum disorder, the diagnosis is made at a later time. The large-scale automatic early screening of the infants with the high-risk autism spectrum disorder below 24 months becomes an important problem to be solved urgently before early intervention.
Currently, traditional screening of children with high risk autism spectrum disorder relies on questionnaires and manual interviews of experienced physicians to determine whether the children are high risk autism spectrum disorder comprehensively based on multiple autism-related assessment scales. The existing high-risk autism spectrum disorder manual assessment scales comprise infant communication and symbolic behavior development scales, childhood autism assessment scales and autism childhood behavior assessment scales. The existing manual evaluation technology has the following technical bottlenecks:
(1) The behavior observation of the infants in the manual scale depends on manual evaluation, and the behavior evaluation is time-consuming and is influenced by subjective evaluation results of evaluators;
(2) The accuracy of manual scale assessment is affected by the experience of the physician, and diagnosis deviation exists;
(3) The number of doctors is limited, and the current assessment means cannot be suitable for large-scale early screening of the high-risk autism spectrum disorder infants within 24 months of age.
Disclosure of Invention
Aiming at the current technical situation, the invention provides a system capable of automatically and objectively quantitatively evaluating facial expression behaviors of infants under a static face paradigm condition, so that the identification result of the high-risk autism spectrum disorder of the infants within 24 months of age is automatically output.
In order to achieve the technical purpose, the invention adopts the following technical scheme:
an automatic detection method of autism spectrum disorder based on video expression behavior analysis is used for automatic screening of high-risk autism spectrum disorder within 24 months, and comprises the following steps:
s1, video acquisition: acquiring infant expression behavior video data in the face-to-face interaction process of an infant and a caregiver during a static face period of a static face paradigm;
s2, face preprocessing: detecting the face position of the tested infant in the video, cutting out a face area, and correcting faces with different postures;
s3, extracting expression behavior index features: extracting textural features from the preprocessed face image, classifying the input textural features to obtain a face motion unit identification result, taking the face motion unit identification result as an input signal, counting the face motion unit identification result according to the window length in a time dimension to obtain segmented expression statistical features, and further taking the segmented expression statistical features as behavior index features, wherein the method specifically comprises the following steps:
s3.1, extracting directional gradient histogram features of the face image;
s3.2, classifying the face motion units: identifying 18 human face motion unit results of human faces;
s3.3, expressing the result as expression recognition result vector X of each frame by analyzing 18 human face motion unit results of each frame of image in the video i =[y i1 ,y i2 ,…,y ij ,…,y i18 ] T Where i denotes the ith frame of the video, y ij Representing the result of recognition of the jth class of face motion units in the ith frame, y ij The method belongs to {0,1}, T is the video duration of 60 seconds, and the total number is 1500 frames;
s3.4, because the autism spectrum disorder child has a certain difference with a normal child in emotional response to sudden face-expressionless and limb-unresponsive care givers, dividing the video with time length T into n video segments according to a certain window length, and observing the difference of two groups of child expression results at each video segment level, so that the significant statistical difference between the recognition results of the face motion unit result AU6 related to positive emotion and the face motion unit result AU17 related to negative emotion between the two groups of the recognition results of each segment can be found, wherein the group average value of the total occurrence time length of the face motion unit result AU6 related to positive emotion in the normal development group is lower than that of the high-risk autism spectrum disorder group, the group average value of the total occurrence time length of the face motion unit result AU17 related to negative emotion is higher than that of the high-risk autism disorder group, and the high-risk autism spectrum disorder group can be distinguished according to the difference of the occurrence time lengths of the two types of the face motion unit AU results;
s3.5, each segment is S in length, and the result AU of the j-th type face motion unit has the segment statistical characteristic result in the kth window lengthBehavior feature matrix of whole videoSplicing the matrix by rows to form a vector V epsilon R 1×18n ;
S4, risk prediction of the high-risk autism spectrum disorder: on the basis of the expression behavior index characteristics of sectional statistics, a support vector machine classifier is adopted to train the V vectors, a strategy of leaving one person for cross validation is adopted in the training process, namely, data of one person is left each time as a test, data of the rest persons are used for training, and a polynomial kernel is adopted in the experiment:
determining the optimal parameter value of a classifier through a cross-validation strategy, on the basis, using all data of two groups of infants collected in the research to train a high-risk autism spectrum disorder detection model, collecting expression videos of the infants during the period of the face being still of a still face caregiver in a still face paradigm for a new infant, identifying 18 types of face motion unit results of each frame in the videos, calculating segmented expression statistical characteristics according to the steps S3.4 and S3.5, and finally inputting the test characteristics into the trained model to predict the risk of the high-risk autism spectrum disorder.
S5, screening report: reporting the expression recognition result of the infant in the process of 1 minute and reporting the risk coefficient of the high-risk autism spectrum disorder.
The step S1 of video acquisition specifically includes:
s1.1, analyzing the infant, wherein the infant is within 24 months of age, the infant is seated in the infant chair, a caregiver and the infant are seated facing each other, and the head of the caregiver is approximately as high as the head of the infant;
s1.2, the interaction is divided into 2 stages, in the first 2 minutes, a caregiver amuses the child by means of speech, expression, limb movement and the like, in the later stage, the caregiver keeps the face expressionless and does not have any speech and limb movement, meanwhile, the facial expression and behavior of the child in the later stage are recorded, the time length is 60 seconds, and the sampling frame rate is 25 frames/second.
The step S2 of face preprocessing specifically includes:
s2.1, performing face detection on each frame of the expression video;
and S2.2, cutting the face image by using the detected candidate frame to obtain a face area with a background removed, and correcting the face image, specifically, positioning face characteristic points, correcting the face and normalizing the face size, wherein the size of the face normalization is 112 multiplied by 112 pixels.
The 18 types of face motion unit results include AU1,
AU2,AU4,AU5,AU6,AU7,AU9,AU10,AU12,AU14,AU15,AU17,AU20,AU23,AU25,AU26,AU28,AU45。
the invention further discloses an autism spectrum disorder automatic detection device based on video expression behavior analysis, which comprises
The video acquisition module: acquiring infant expression behavior video data in the face-to-face interaction process of an infant and a caregiver during a static face period of a static face paradigm;
a face preprocessing module: detecting the face position of the tested infant in the video, cutting out a face area, and correcting faces with different postures;
expression behavior index feature extraction module: extracting texture features from the preprocessed face image, classifying the input texture features to obtain a face motion unit identification result, taking the face motion unit identification result as an input signal, counting the face motion unit identification result according to the window length in a time dimension to obtain segmented expression statistical features, and further taking the segmented expression statistical features as behavior index features;
high-risk autism spectrum disorder risk prediction module: and predicting whether the infant is the high-risk autism spectrum disorder or not by utilizing the segmented emotional statistical characteristics and the support vector machine classifier.
A screening report module: reporting the expression recognition result of the infant in the process of 1 minute and reporting the risk coefficient of the high-risk autism spectrum disorder.
The invention has the advantages that:
firstly, the risk of the infant high-risk autism spectrum disorder can be automatically analyzed and judged based on the infant facial expression behavior data within 1 minute during the stationary face in the stationary face paradigm, the experiment time is long, no contact type cheap equipment is needed, the operation is simple and easy, and the method is suitable for large-scale automatic screening of the high-risk autism spectrum disorder;
second, compared with manual evaluation, the present invention has the advantages of high reliability based on quantitative evaluation results and high efficiency based on automatic evaluation results.
Drawings
FIG. 1 is a block diagram of the framework and the method of the present invention;
FIG. 2 is an acquisition scenario arrangement of the method of the present invention;
FIG. 3 is a block statistical expression behavior feature diagram of the method of the present invention;
FIG. 4 is a schematic representation of prediction of high risk autism spectrum disorder in the methods of the invention.
Detailed Description
According to the result of artificial observation of the facial behaviors of the high-risk autism spectrum disorder infants in the static face model, the duration and the frequency of positive emotions of the autism spectrum disorder infants and the normally-developing infants in the static face experiment are obviously different. In addition, in the aspect of automatic evaluation of expressions, psychologists have developed a Facial motion Coding System (FACS), and Facial expressions are products of deformation of Facial motion units. Based on the artificial observation result of the infant emotion caused by the autism and a FACS coding system, the automatic analysis result of the human face movement unit is used as the behavior characteristic for the automatic detection of the autism spectrum disorder.
The specific implementation process of the invention is as follows:
s1, video acquisition: and (3) shooting the emotional response video of the infant within 2 years of age when the infant care giver keeps the face expressionless for 1 minute under the static face paradigm. The infant sits in the infant chair, and the caretaker and the infant sit facing each other, and the head of the caretaker is approximately as high as the head of the infant; the interaction is divided into 2 stages, in the first 2 minutes, the caretaker amuses the child by means of speech, expression, limb movement and the like, in the later stage, the caretaker keeps the face expressionless and keeps the absence of any speech and limb movement, and meanwhile, the facial expression and the behavior of the child in the later stage are recorded for 60 seconds;
the invention adopts a 1-path camera to capture the facial expression of the front face of the infant, the camera facing the infant is positioned at the position about 40cm in front of the infant, and the height of the camera is about 20cm higher than the top of the head of the infant when the infant sits. The acquisition scenario is shown in fig. 2.
The invention collects the facial expression video data of 37 high-risk autism spectrum disorder infants and 49 normal-development infants.
S2, face preprocessing: face detection, face feature point positioning, face correction and size normalization operations
Face detection: aiming at the face detection of a current frame (starting from a second frame) in a video, the face detection frame output by an MTCNN face technology (three-stage detection threshold values are respectively set as [0.6,0.7,0.8 ]) is expanded by 1.5 times by utilizing the face position prior information of the previous frame in the video to obtain a candidate area of a face, the face of the current frame is detected in the expansion area, if the face is not detected, the MTCNN is reduced by 10 percent from the face threshold value of the coarse stage to the face threshold value of the fine stage until the face detection is successful, and the strategy can achieve efficient and more stable face detection performance.
Positioning face feature points: detecting 68 characteristic points of the human face by using a Constrained Local Models (CLMs) technology;
face correction and size normalization: on the basis of 68 individual face feature points, the detected faces are registered to a face standard template (112 × 112), and the faces are corrected and normalized to 112 × 112 pixels, so that background interference except the faces is eliminated.
S3, expression behavior index feature extraction:
extracting Histogram of Oriented Gradients (HOG) features from the normalized face image;
according to a Facial motion unit Coding System (FACS) developed by psychologists, 18 Facial motion units in Facial expression videos of children are analyzed, and the method comprises the following steps: AU1, AU2, AU4, AU5, AU6, AU7, AU9, AU10, AU12, AU14, AU15, AU17, AU20, AU23, AU25, AU26, AU28, AU45, the invention adopts Openface open source tool to analyze the human face movement unit;
expressing the result as expression recognition result vector X of each frame by analyzing 18 facial motion unit results of each frame of image in the video i =[y i1 ,y i2 ,…,y ij ,…,y i18 ] T Where i denotes the ith frame of the video, y ij Denotes the result of recognition of the ith frame of an AU of the jth class, y ij The method belongs to {0,1}, T is the video duration of 60 seconds, and the total number is 1500 frames;
dividing a video with the time length of T into n (n = 4) video segments according to a certain window length, and judging a high-risk autism pedigree disorder group and a normal development group according to the difference of the occurrence time lengths of AU6 and AU 17;
the segment statistical characteristic result in the k window length of the j-th AU with each segment length of SBehavior feature matrix of the entire video->Splicing the matrix by rows to form a vector V epsilon R 1×18n 。
S4, risk prediction of high-risk autism spectrum disorder:
on the basis of expression behavior index features of sectional statistics, a support vector machine (polynomial kernel) classifier is adopted to train the V vectors, and a strategy of leaving one person for cross validation is adopted in the training process, namely, data of one person is left each time to be used as a test, and data of the rest persons are used for training. The experiment used a polynomial kernel:
Wherein γ =0.5, r =0, d =3;
determining the optimal parameter value of a classifier through a cross-validation strategy, on the basis, using all data of two groups of infants collected in the research for training a high-risk autism spectrum disorder detection model, collecting expression videos of the infants during the static face period of a static face paradigm caregiver, identifying 18 types of face motion unit results of each frame in the videos, calculating segmented expression statistical characteristics according to the S3 step, and finally inputting the test characteristics into the trained model for high-risk autism spectrum disorder risk prediction.
S5, screening report: reporting the expression recognition result of the infant in the process of 1 minute and reporting the risk coefficient of the high-risk autism spectrum disorder.
The method has the advantages that the classification accuracy rate reaches 88.37%, the sensitivity is 83.78%, and the specificity is 91.84%. The cross-validation classification confusion matrix of the leave-one-person method is shown in table 1.
TABLE 1 Classification result confusion matrix
The invention further discloses an autism spectrum disorder automatic detection device based on video expression behavior analysis, which comprises
The video acquisition module: acquiring infant expression behavior video data in the face-to-face interaction process of an infant and a caregiver during a static face period of a static face paradigm;
a face preprocessing module: detecting the face position of the tested infant in the video, cutting out a face area, and correcting faces with different postures;
the expression behavior index feature extraction module: extracting textural features from the preprocessed face image, classifying the input textural features to obtain a face movement unit identification result, taking the face movement unit identification result as an input signal, counting the face movement unit identification result according to the window length in a time dimension to obtain segmented expression statistical features, and further taking the segmented expression statistical features as behavior index features;
high-risk autism spectrum disorder risk prediction module: and predicting whether the infant is the high-risk autism spectrum disorder or not by utilizing the segmented emotional statistical characteristics and the support vector machine classifier.
The screening report module: reporting the expression recognition result of the infant in the process of 1 minute and reporting the risk coefficient of the high-risk autism spectrum disorder.
The above embodiments are only intended to illustrate the technical solution of the present invention and not to limit the same, and a person skilled in the art can make modifications or equivalent substitutions to the technical solution of the present invention without departing from the spirit and scope of the present invention, and the scope of protection of the present invention should be subject to the claims.
Claims (4)
1. An automatic detection method of autism spectrum disorder based on video expression behavior analysis is used for automatic screening of high-risk autism spectrum disorder within 24 months, and is characterized by comprising the following steps:
s1, video acquisition: acquiring infant expression behavior video data in the face-to-face interaction process of an infant and a caregiver during a static face period of a static face paradigm;
s2, face preprocessing: detecting the face position of the tested infant in the video, cutting out a face area, and correcting faces with different postures;
s3, expression behavior index feature extraction: extracting texture features from the preprocessed face image, classifying the input texture features to obtain a face motion unit recognition result, taking the face motion unit recognition result as an input signal, counting the face motion unit recognition result according to the window length in a time dimension to obtain segmented expression statistical features, and further taking the segmented expression statistical features as behavior index features, wherein the method specifically comprises the following steps:
s3.1, extracting directional gradient histogram features of the face image;
s3.2, classifying the face motion units: identifying 18 human face motion unit results of human faces;
s3.3, expression recognition result vectors of each frame are expressed by analyzing 18 human face movement unit results of each frame of image in the videoWhere i denotes the ith frame of the video, y ij Represents the result recognized by the jth class face motion unit in the ith frame, and>t is the video duration of 60 seconds, and the total number is 1500 frames;
s3.4, because the emotional response of the autism spectrum disorder baby to the sudden face-expressionless and limb-unresponsive of the caregiver is different from that of the normal baby, dividing the video with the time length T into n video segments according to a certain window length, and observing the difference of two groups of baby expression results at each video segment level, the statistical difference of the recognition result of each segment between the two groups of baby expression results can be found, wherein the group average value of the total occurrence time length of the face motion unit result AU6 related to positive emotion in the normal development group is lower than that of the high-risk autism spectrum disorder group, the group average value of the total occurrence time length of the face motion unit result AU17 related to negative emotion is higher than that of the high-risk autism disorder group, and the high-risk autism spectrum disorder group and the normal development group can be distinguished according to the difference of the occurrence time lengths of the two groups of face motion unit AU results;
s3.5, each segment length is S, the segment statistical characteristic result in the kth window length of the jth type human face motion unit result AU,k/>Behavior feature matrix of the entire videoThe matrix is spliced line by line into a vector V ÷ based>;
S4, risk prediction of the high-risk autism spectrum disorder: on the basis of the expression behavior index characteristic of sectional statistics, adopt support vector machine classifier to train above-mentioned vector V, adopt in the training process and leave one person's cross validation's strategy, leave one person's data as the test each time promptly, remaining people's data is used for training, and the polynomial core is adopted in the experiment:
Determining the optimal parameter value of a classifier through a cross validation strategy, on the basis, using all data of two groups of infants collected in the research to train a high-risk autism spectrum disorder detection model, collecting expression videos of the infants during the period of the face being still of a still face caregiver in a still face paradigm for a new infant, identifying 18 types of face motion unit results of each frame in the videos, calculating segmented expression statistical characteristics according to the steps S3.4 and S3.5, and finally inputting the statistical characteristics into the trained model to predict the risk of the high-risk autism spectrum disorder;
s5, screening report: reporting the expression recognition result of the infant in the 1-minute process, and reporting the risk coefficient of the high-risk autism spectrum disorder;
the step S1 of video acquisition specifically includes:
s1.1, analyzing the infant, wherein the infant is within 24 months of age, the infant is seated in the infant chair, a caregiver and the infant are seated facing each other, and the head of the caregiver is approximately as high as the head of the infant;
s1.2, the interaction is divided into 2 stages, in the first 2 minutes, a caregiver amuses the child by means of speech, expression, limb movement and the like, in the later stage, the caregiver keeps the face expressionless and does not have any speech and limb movement, meanwhile, the facial expression and behavior of the child in the later stage are recorded, the time length is 60 seconds, and the sampling frame rate is 25 frames/second.
2. The method for automatically detecting autism spectrum disorder based on video expression behavior analysis as claimed in claim 1, wherein the step S2 of face preprocessing specifically comprises:
s2.1, performing face detection on each frame of the expression video;
s2.2, cutting the face image by using the detected candidate frame to obtain a face area with a background removed, and correcting the face image, specifically, positioning the face characteristic points, correcting the face and normalizing the face size, wherein the size of the face normalization is 112112 pixels.
3. The method of claim 1, wherein the 18 facial motion unit results include AU1, AU2, AU4, AU5, AU6, AU7, AU9, AU10, AU12, AU14, AU15, AU17, AU20, AU23, AU25, AU26, AU28, and AU45.
4. The detection device of the automatic autism spectrum disorder detection method based on the video expression behavior analysis, as claimed in claim 1, comprises:
the video acquisition module: acquiring infant expression behavior video data in the face-to-face interaction process of an infant and a caregiver during a static face period of a static face paradigm;
a face preprocessing module: detecting the face position of the tested infant in the video, cutting out a face area, and correcting faces with different postures;
the expression behavior index feature extraction module: extracting texture features from the preprocessed face image, classifying the input texture features to obtain a face motion unit identification result, taking the face motion unit identification result as an input signal, counting the face motion unit identification result according to the window length in a time dimension to obtain segmented expression statistical features, and further taking the segmented expression statistical features as behavior index features;
high-risk autism spectrum disorder risk prediction module: predicting whether the infant is high-risk autism spectrum disorder or not by utilizing the segmented emotion statistical characteristics and the support vector machine classifier;
the screening report module: reporting the expression recognition result of the infant in the process of 1 minute and reporting the risk coefficient of the high-risk autism spectrum disorder.
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