A Facial Expression Recognition Method Based on Gabor Wavelet and Gray Level Co-occurrence Matrix

Abstract

A facial expression recognition method based on Gabor wavelet and gray-level co-occurrence matrix. This method first extracts the "pure face" area in the expression image by manual segmentation, and then performs gray-scale color image, histogram equalization and Scale normalization processing; then through a method of extracting Gabor feature statistics in blocks, the data redundancy of traditional Gabor features is greatly reduced, and the gray level co-occurrence matrix reflecting image texture features is introduced into the field of expression recognition for the first time to This is to make up for the lack of pixel spatial correlation caused by Gabor feature block processing; finally a set of low-dimensional feature vectors for feature expression is generated, which greatly improves the expression recognition rate on the premise of ensuring a high expression recognition rate .

Description

technical field [0001] The invention relates to the field of human facial expression recognition, and designs and implements a human facial expression recognition algorithm based on Gabor wavelet and gray level co-occurrence matrix. Background technique [0002] Facial expressions contain rich personal emotional information and are an important way for humans to express emotions. By recognizing facial expressions, computers can understand the corresponding psychological state of humans, so as to better serve all aspects of human life, such as human-computer interaction, intelligent security, medical monitoring, psychological analysis, etc. At present, most expression recognition algorithms are mainly tested on facial expression images with no occlusion in front, and the complete expression information is preserved to a certain extent. However, in real life, facial expressions are often blocked. Coverage by hair, sunglasses, masks, gestures, etc. will cause the loss of facia...

AI Technical Summary

Problems solved by technology

[0005] However, since the expression features extracted by Gabor wavelets often have high dimensions, various feature extraction algorithms combined with Gabor wavelets have the disadvantages of large amount of calculation and high complexity, resulting in a long time-consuming process of the entire expression recognition process. It is not conducive to expression recognition from experimental simulation to practical application

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