Face identification method based on Gabor wavelet and SB2DLPP

Abstract

The invention discloses a face identification method based on Gabor wavelet and SB2DLPP. The face identification method based on the Gabor wavelet and SB2DLPP mainly includes pre-treatment, feature extraction, feature dimension reduction and classification identification, and to be specific, the face identification method includes that (1) pre-treating all the face images in a known face database, wherein the pre-treatment includes scale normalization and histogram equalization; (2) using the Gabor wavelet to extract features of the pre-treated face images; (3) leading in class information, and using a supervised bidirectional two-dimensional local preserving projection (SB2DLPP) algorithm to reduce the dimensions of the high-dimensional image features extracted through the step (2) to extract feature matrices mapped to a low-dimensional sub-space; (4) using a nearest neighbor classifier to perform classification identification. The face identification method based on the Gabor wavelet and SB2DLPP uses the Gabor wavelet and improved SB2DLPP algorithm to identify images, the problems that a traditional face identification method is easy to be influenced by light, expression and the like external factors are overcame, and the face identification rate is effectively improved.

Description

technical field [0001] The invention belongs to the technical fields of image processing, computer vision and pattern recognition, in particular to a face recognition method based on Gabor wavelet and SB2DLPP. Background technique [0002] Face recognition technology is a technology that extracts facial features through computers, and uses these features for identification and verification. As an important biological feature, human face conveys very important information. Because of its unique characteristics, it can convey individual identity information, so face recognition technology is the fastest-growing biometric recognition technology in recent years. At present, face recognition has broad application prospects in credit card identification, security inspection, monitoring and other fields. However, due to the complexity of the face structure, the face changes caused by expressions, age and posture, and the variability caused by the illumination and shooting angles ...

AI Technical Summary

Problems solved by technology

[0005] However, these algorithms are all vector-based, and need to convert the two-dimensional image matrix into a one-dimensional vector first

This conversion usually leads to two problems: one is to lose some important structural information; but high-dimensional vectors and limited samples will face singular value problems

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