Fuzzy discriminant analysis method applied to facial expression recognition

Abstract

The invention proposes a fuzzy discriminant analysis method applied to facial expression recognition. Category membership of each training sample is designed by adopting a fuzzy K nearest neighbor method (Fuzzy KNN); on the basis, a canonical correlation analysis (CCA) method is applied to finding a solution so as to enable the training samples and the corresponding fuzzy category membership thereof to have the projection direction of maximum correlation; relation between input data and corresponding category membership data is established by adopting a least-squares regression (LSR) method; and then category indexes are established according to the category membership data. In order to improve recognition results, the method can be spread to nuclear space through kernel functions. The method provides an effective means to estimate the degree of the possibility that a to-be-tested sample belongs to a certain category, and solves the problem that a large number of actual facial-expression samples cannot be simply attributed to a certain category. Experiments show that the method has better recognition performance compared with the prior expression recognition method.

Description

technical field [0001] The invention relates to a fuzzy discriminant analysis method, in particular to a fuzzy discriminant analysis method applied to facial expression recognition. Background technique [0002] At present, the linear discriminant analysis (LDA) method has been successfully applied to many pattern recognition problems, such as face recognition, image restoration, facial expression recognition and so on. Traditional discriminant analysis methods require each training sample to uniquely belong to a certain category of pattern types. However, in practice, some pattern samples cannot simply be assigned to a certain category. In this case, the traditional discriminant analysis method can no longer be well applied. For example, when doing facial expression recognition, each facial image may contain information about all six basic expressions (happy, sad, surprised, angry, disgusted, and scared). Therefore, it is unreasonable to simply classify each face image i...

AI Technical Summary

Problems solved by technology

Some current methods do not effectively solve these problems, and the research method of applying the fuzzy discrimination method of canonical correlation analysis to expression recognition is not mature enough.

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