Facial expression recognition method

Abstract

The invention discloses a facial expression recognition method. According to the method, texture feature extraction is carried out on a facial expression image by using a multi-scale-parameter MB-LBPoperator and unified-mode pixel histogram statistics is carried out; HOG feature extraction is carried out on facial image samples; series-connection feature fusion is carried out on MB-LBP features and the HOG features in a same-kind mode; in fused feature space, a training sample is extracted randomly and the rest of samples are used as testing sample; PCA dimensionality reduction calculation iscarried out on the training sample to obtain a projection matrix W, the training sample is projected to low-dimensional sub space to obtain a feature expression of an expression image in the low-dimensional sub space; and the testing samples are projected to the low-dimensional sub space by the projection matrix W and the features of the testing samples are classified by using a sparse representation classifier to obtain the types of the testing samples. Therefore, an expression feature with texture and shape information is expressed by using a low dimension number, so that a high expressionrecognition rate is obtained; and the recognition accuracy is high.

Description

technical field [0001] The invention belongs to image classification technology, in particular to a facial expression recognition method based on the fusion of MB-LBP unified mode histogram and HOG feature. Background technique [0002] Expression is one of the important ways to convey emotion in interpersonal communication. Facial expression recognition refers to the use of computer to extract facial expression features from detected faces, so that the computer can understand and process human facial expressions according to human thinking and understanding. , and can respond according to people's needs, and establish a friendly and intelligent human-computer interaction environment. This research is a hotspot at the forefront of interdisciplinary research in image processing, pattern recognition, psychology, affective computing, and computer vision. [0003] Facial expression recognition is mainly composed of three parts: face detection, expression feature extraction and ...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is to adopt the method of multi-feature fusion and then data dimensionality reduction for the situation where the effect of single feature extraction is not obvious, to fuse effective identification information of multiple features, to realize effective compression of information, and to improve calculation efficiency

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