Facial expression identification method based on multi-task convolutional neural network

Abstract

The invention discloses a facial expression identification method based on multi-task convolutional neural network. The expression identification method comprises the following steps: firstly, designing a multi-task convolutional neural network structure, and sequentially extracting low-level semantic features shared by all expressions and a plurality of single-expression distinguishing characteristics in the network; then adopting multi-task learning and simultaneously learning learning tasks of the plurality of single-expression distinguishing characteristics and multi-expression identification tasks; monitoring the all tasks of the network by using combined loss, and balancing the loss of the network by using the two loss weights; finally, acquiring a final facial expression identification result from a maximum flexible classification layer arranged at the last of a model according to the trained network model. Characteristic extraction and expression classification are put in an end-to-end framework to be learned, the distinguishing characteristics are extracted from input images, and expression identification on the input images are reliably carried out. Experimental analysisshows that the algorithm is excellent in performance, complicated facial expressions can be effectively distinguished, and good identification performance on a plurality of published data sets can beachieved.

Description

Technical field [0001] The invention relates to computer vision technology, in particular to a facial expression recognition method based on a multi-task convolutional neural network. Background technique [0002] In the past few decades, automatic facial expression recognition has attracted more and more computer vision experts and scholars. The goal of facial expression recognition is to design a system for a given facial expression picture that can automatically predict the facial expression category it belongs to. The facial expression automatic recognition technology has a wide range of application scenarios, such as human-computer interaction, safe driving, and medical care. Although this technology has achieved considerable success over the years, it is still a huge challenge to perform reliable automatic facial expression recognition under uncontrollable environmental conditions. [0003] A facial expression recognition system includes three modules: face detection, featu...

AI Technical Summary

Problems solved by technology

The intra-class loss can effectively reduce the intra-class difference of features, however, the intra-class loss does not explicitly expand the inter-class difference of features

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