Face recognition method based on weighted collaborative representation

Abstract

The invention discloses a face recognition method based on weighted collaborative representation, and the method comprises the steps: to-be-recognized images are linearly represented as a linear combination of all training images, and distance information of a to-be-recognized image and each type of sample serves as prior information to be introduced into a feature representation function; The reconstruction weights of a certain type of samples closer to the to-be-recognized images are enhanced, then the least square method is utilized to solve the representation coefficient, and finally the type of each to-be-recognized image is judged according to the reconstruction residual error between each to-be-recognized image and each type of training image. The optimization problem is solved based on an L2 norm, so that calculation speed is relatively high, in addition, the category information of training samples and the priori distance information between each to-be-recognized sample and each type of training samples are used as weights for constructing a feature representation equation, so that the feature expression capability of the proposed model can be enhanced to a certain extent;therefore, the influence of changes of image illumination, face postures, expressions and the like on the recognition effect can be effectively avoided.

Description

technical field [0001] The invention relates to an image recognition method, in particular to a face recognition method based on weighted collaborative representation, belonging to the technical field of image recognition. Background technique [0002] Face recognition is an important method of identity identification, and has broad application prospects in file management systems, security verification systems, credit card verification, criminal identification in public security systems, monitoring of banks and customs, and human-computer interaction. In the past few decades, researchers have proposed many face recognition methods, among which image classification methods based on representation learning are widely used in face recognition. [0003] The more famous image classification methods based on representation learning are: [0004] (1) Sparse Representation Classifier (SRC), described in J.Wright, A.Y.Yang, A.Ganesh, S.S.Sastry, Y.Ma published in 2009 in IEEE Trans...

AI Technical Summary

Problems solved by technology

However, LRC assumes that the image to be recognized can be linearly represented by a certain type of samples, and its feature expression ability is related to the number of samples of each type. When the number of samples of a certain type is small, its feature expression ability is weak and the recognition ability will also decline.

When CRC uses the entire training image to linearly represent the image to be recognized, it does not consider the differences between samples of different categories, resulting in weak feature expression ability and the recognition ability will also be affected.

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