Method for identifying facial expressions from human face image sequence

Abstract

The invention relates to a method for identifying facial expressions from a human face image sequence, belonging to the technical field of analyzing and identifying human facial expressions. The method of the invention comprises the following steps of: firstly, adopting a method for tracing feature points, sequentially extracting the displacement amount of the normalized facial key point and the length of the special geometrical characteristic for each frame image of the expression image sequence, and combining the data to form a characteristic column vector; secondly, sequentially arranging all characteristic column vectors of the sequence to form a characteristic matrix, wherein each characteristic matrix represents a facial expression image sequence; finally, comparing the similarities among the characteristic matrixes by using a canonical correlation analysis method, thereby determining the human face images to be identified into one of the basic expressions of happiness, sadness, fear, hate, surprise and anger. In the invention, the canonical correlation analysis method is successfully applied to identifying the human facial expressions, the dynamic information in the expression generation course is utilized effectively and the higher recognition rate and the shorter CPU computation time are acquired.

Description

technical field [0001] The invention relates to a method for recognizing facial expressions from a human face image sequence, and belongs to the technical field of human facial expression analysis and recognition. Background technique [0002] With the rapid development of computer technology, automatic facial expression analysis and recognition technology will make facial expression a new channel for human-computer interaction, and make the interaction process more natural and effective. Facial expression analysis and recognition involves three basic issues: ① how to find and locate human faces in images; ② how to extract effective expression features from detected facial images or facial image sequences; ③ how to design appropriate classification method to identify expression types. In recent years, a lot of research work has been devoted to recognizing facial expressions from image sequences: Cohn et al. in the document "Feature-Point Tracking by Optical Flow Discriminat...

AI Technical Summary

Problems solved by technology

The disadvantage of these methods is that they only extract features from peak expression frames, ignoring the important temporal dynamic information contained in the expression generation process, so the recognition accuracy is not high

However, in the expression recognition problem, the difference between the facial images of the same person with different expressions is not large, even the images of two opposite expressions are not very different, so simply applying canonical correlation analysis techniques to expression recognition cannot achieve better results

So far, no relevant literature and practical application of canonical correlation analysis technology in facial expression recognition have been found.

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