Identification method for human facial expression based on two-step dimensionality reduction and parallel feature fusion

Abstract

The invention requests to protect an identification method for a human facial expression based on two-step dimensionality reduction and parallel feature fusion. The adopted two-step dimensionality method comprises the following steps: firstly, respectively performing the first-time dimensionality reduction on two kinds of human facial expression features to be fused in the real number field by using a principal component analysis (PCA) method, then performing the parallel feature fusion on the features subjected to dimensionality reduction in a unitary space, secondly, providing a hybrid discriminant analysis (HDA) method based on the unitary space as a feature dimensionality reduction method of the unitary space, respectively extracting two kinds of features of a local binary pattern (LBP) and a Gabor wavelet, combining dimensionality reduction frameworks in two steps, and finally, classifying and training by adopting a support vector machine (SVM). According to the method, the dimensions of the parallel fusion features can be effectively reduced; besides, the identification for six kinds of human facial expressions is realized and the identification rate is effectively improved; the defects existing in the identification method for serial feature fusion and single feature expression can be avoided; the method can be widely applied to the fields of mode identification such as safe video monitoring of public places, safe driving monitoring of vehicles, psychological study and medical monitoring.

Description

technical field [0001] The invention relates to the field of facial expression recognition in pattern recognition, in particular to a facial expression recognition method based on two-step dimensionality reduction and parallel feature fusion. Background technique [0002] Facial expression recognition technology, also known as automatic facial expression recognition technology, refers to the use of program algorithms to enable machines to automatically recognize different types of facial expressions, that is, through training and learning data, it has the ability to understand and recognize faces The ability to express. Psychologists believe that emotional expression = 7% language + 38% voice + 55% facial expression. It can be seen that facial expression can well reflect a person's emotional expression. It can be said that facial expressions are the most important carrier of human emotions and the external manifestation of human inner world. As an important part of artific...

AI Technical Summary

Problems solved by technology

The traditional serial feature fusion method is to connect a variety of feature data end to end to form a serial combination feature. The dimension of the fused feature is the sum of the dimensions, so it is easy to cause the disaster of dimensionality, and it will affect the speed of subsequent training and classification. Serious impact

In addition, the dimensionality of high-dimensional data will further deepen the problem of matrix singularity caused by the high-dimensional small sample problem, thus affecting the solution of the dimensionality reduction projection axis

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