A Facial Expression Recognition Method Based on Two-step Dimensionality Reduction and Parallel Feature Fusion

Abstract

The present invention claims to protect a facial expression recognition method based on two-step dimensionality reduction and parallel feature fusion, which adopts two-step dimensionality reduction: first, uses principal component analysis (PCA) to separately treat the fusion of two types of facial expression features in the real number domain Carry out the first dimensionality reduction, and then perform parallel feature fusion on the reduced dimensionality features in the unitary space; secondly, a hybrid discriminant analysis method (HDA) based on the unitary space is proposed as a feature dimensionality reduction method in the unitary space, by Two types of features, Local Binary Pattern (LBP) and Gabor Wavelet, are extracted for facial expressions, combined with the above two-step dimensionality reduction framework, and finally support vector machine (SVM) is used for classification and training. The method can effectively reduce the dimensionality of parallel fusion features, simultaneously realize the recognition of six basic facial expressions and effectively improve the recognition rate. This method can avoid various drawbacks in serial feature fusion and single-feature facial expression recognition methods, and can be widely used in pattern recognition fields such as public security video monitoring, vehicle safety driving monitoring, psychological research, 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

Images