Two-dimension linearity discrimination analysis face identification method

Abstract

The invention discloses a two-dimension linearity discrimination analysis face identification method, comprising steps of calculating an affine matrix element according to a chosen training sample matrix, calculating an intra-class divergence matrix and an extra-class divergence matrix according to the affine matrix element, calculating the characteristic value and the characteristic vector of a matrix of the product between the inverse of the intra-class divergence matrix and the extra-class divergence matrix according to the intra-class divergence matrix and the extra-class divergence matrix, solving a projection matrix, using the projection matrix to project the training sample matrix to the projection space to obtain the projected matrix, adopting a neighbor classifier to perform classification processing on the projected matrix and the test sample, and calculating the recognition rate. The invention fuses the advantages of two-dimension linearity discrimination analysis and the two-dimension local projection maintaining method, which not only performs effective dimension reduction on the original data, but also maintain the local characteristics of the data. Furthermore, the invention avoids the fact that the two-dimension structure of the sample data is damaged when the sample data is stretched into one-dimension data, and has high recognition rate and high efficiency.

Description

technical field [0001] The invention relates to the field of biological pattern recognition, in particular to a face recognition technology. Background technique [0002] With the development of computer science and biomedical technology, the use of human biometrics for identification has become an important way. As an important part of biometric technology, face recognition technology has become the most commonly used means of identification in people's daily life. It not only has the universality, security, uniqueness, stability and collectability At the same time, it has the advantages of no need for target cooperation, long-distance execution and intuitive comparison. Therefore, face recognition technology has been widely used in information security, criminal investigation, entrance and exit monitoring and other fields. [0003] Existing face recognition methods include: Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel-based Feature Extra...

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problems existing in the existing linear discrimination analysis method, and propose a face recognition method of two-dimensional linear discrimination analysis, which can directly perform calculation processing on the two-dimensional face data without destroying the face data structure, which simplifies the calculation process and improves the face recognition rate

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