Face sequence expression recognition method based on deep learning

Abstract

The invention provides a face sequence expression analysis method based on deep learning and mainly relates to classification of face sequence expressions through a multi-scale face expression recognition network. The face sequence expression analysis method comprises the steps that the multi-scale face expression recognition network (comprising three channels for processing face sequences with different resolution ratios of 128*128, 224*224, 336*336 and the like) is constructed, features in the face sequences with the different resolution ratios are parallelly extracted through the network, finally, the three kinds of features are fused, and thus classification of the face sequence expressions is obtained. According to the face sequence expression analysis method based on deep learning, the self-learning capability of deep learning is given to full play, limitation of manual feature extraction is avoided, and thus the adaptability of the method is higher; and parallel training and predicting are conducted through the structural features of a multi-stream deep learning network, finally, the classification results of a plurality of sub-networks are fused, the accuracy rate is increased, and the working efficiency is improved.

Description

technical field [0001] The invention relates to the recognition of facial sequence expressions in the field of video analysis, in particular to a video analysis method for classifying facial sequence expressions based on a deep learning multi-stream neural network. Background technique [0002] Facial expression is one of the important features of human emotion recognition. Darwin introduced this field as an area of ​​study in his book The Expression of Emotions in Man and Animals. Facial expression recognition refers to the separation of specific expression states from a given static image or dynamic video sequence, so as to determine the psychological emotion of the recognized object. At present, automatic facial expression recognition has a wide range of applications, such as data-driven animation, neuromarketing, interactive games, social robots, and many other human-computer interaction systems. [0003] Facial expression recognition can be divided into expression rec...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a method for facial expression recognition in a video sequence, which combines deep learning with video facial expressions to give full play to the advantages of deep learning self-learning, and can solve the difficulty in adjusting the parameters of current shallow learning. Manual selection of features, low accuracy and other issues

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