Abstract
In order to recognize the instantaneous changes of facial microexpressions in natural environment, a method based on optical flow direction histogram and depth multiview network to enhance forest microexpression recognition was proposed. In the preprocessing stage, the histogram equalization of the acquired face image is performed, and then the dense key points of the face are detected. According to the coordinates of the key points and the face action coding system (FACS), the face region is divided into 15 regions of interest (ROI). In the feature extraction stage, the optical flow direction histogram feature between adjacent frames in ROI is extracted to detect the peak frame of microexpression sequence. Finally, the average optical flow direction histogram feature of the image sequence from the initial frame to the peak frame is extracted. In the classification stage, firstly, the head pose parameters under horizontal degrees of freedom are estimated to eliminate the influence of head pose motion, and a forest multiview conditional probability model based on deep multiview network is established. Conditional probability and neural connection function are introduced into the node splitting learning of random tree to improve the learning ability and distinguishing ability of the model on the limited training set. Finally, multiview-weighted voting is used to determine the categories of facial microexpressions. Experiments on CASME II microexpression dataset show that the proposed method can effectively describe the changes of microexpressions and improve the recognition accuracy compared with other new methods.
Highlights
Facial expression is a facial movement that reflects people’s spirit and emotions
Based on the average optical flow direction histogram feature, a conditional probability model of forest multiview enhancement based on depth multiview network was established to recognize microexpressions
In order to reduce the influence of posture on facial expression recognition, nine types of head posture estimation are performed on CASME II dataset using the proposed method
Summary
Facial expression is a facial movement that reflects people’s spirit and emotions. About 55% of information is transmitted through facial expression when people communicate.erefore, it plays an important role in social communication. e efficiency of facial expression recognition is an important part of human-computer interaction, and the related research on facial expression recognition has become a hot spot [1]. About 55% of information is transmitted through facial expression when people communicate. E efficiency of facial expression recognition is an important part of human-computer interaction, and the related research on facial expression recognition has become a hot spot [1]. Literature [3] focuses on such a meaningful topic and investigates how to make full advantage of the color information provided by the microexpression samples to deal with the microexpression recognition (MER) problem. Microexpressions may contain all muscular movements of ordinary expressions and only part of them. It expresses the true feelings that human beings try to hide. Erefore, microexpressions have many potential applications, such as criminal investigation, national defense security, clinical diagnosis, and humancomputer interaction. The characteristics of short duration, low intensity, and usually involving only local motion of microexpressions pose a great challenge to the recognition of microexpressions
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