A face recognition method based on multi-feature description and local decision weighting

Abstract

The invention discloses a face recognition method with multi-feature description and local decision weighting, which includes the following steps: 1. Firstly, use the independent component analysis algorithm to construct a global complementary subspace, and roughly classify the samples to be tested; 2. Use the proposed unified Combined with the other two texture description algorithms to construct a local complementary subspace to obtain the posterior probability value of the difficult-to-recognize sample in rough classification; precise decision-making results. The significance of the present invention is that 1. the present invention can effectively improve the expression ability of facial texture features and enhance the accuracy of feature representation; 2. construct global and local two complementary subspaces to describe human faces, and only cast difficult-to-recognize samples into local Accurate classification on the subspace overcomes the problems of low recognition rate or long recognition time in traditional methods.

Description

technical field [0001] The invention relates to a feature extraction method and classification discrimination, belonging to the field of pattern recognition, in particular to a face recognition method based on multi-feature description and local decision weighting. Background technique [0002] Face recognition is a hot research topic in recent years. The description and classification of face images are the two main steps of face recognition. According to the No Free Lunch (NFL) theorem, there is no single algorithm that can be superior to other algorithms in any case, so research on multi-feature and multi-classifier fusion of images has become the current mainstream development direction. [0003] Using global and local features to describe human faces is a commonly used method at present. The global feature mainly describes and expresses the attributes and information of the whole face, while the local feature mainly describes the information of the details of the facia...

AI Technical Summary

Problems solved by technology

In recent years, many scholars have proposed improvements to the LBP algorithm, which have improved the recognition ability of the algorithm to a certain extent, but they all use the local size relationship between neighboring points to describe the texture information, while ignoring the same square The overall relationship between the upward pixel and the gray value of the central pixel

When there are random noise points or there are lighting and edge changes, the performance will be greatly reduced

[0006] The Symmetric Local Graph Structure (SLGS) operator is a recently proposed texture description algorithm, which is an improvement of MFA Abdullah's LGS algorithm. It is no longer limited to the circular neighborhood, and uses Fewer pixels are used to describe the texture features, but it only considers the horizontal direction of the central pixel point, and does not consider the vertical direction, and the vertical direction also includes a lot of information about the texture

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