Face recognition method based on mutual information parameter-free locality preserving projection algorithm

Abstract

The invention provides a face recognition method based on a mutual information parameter-free locality preserving projection algorithm. According to the method, similarities among samples are calculated through adoption of MI; the similarities are directly taken as similarity coefficients among the samples; moreover, an average similarity of the samples is taken as a demarcation point, the samples are divided into neighbor samples and non-neighbor samples. A locality neighbor similarity divergence matrix and a non-neighbor divergence matrix of the samples are determined based on the neighbor samples and non-neighbor samples. According to the method, the functions of the neighbor samples are taken into consideration, and moreover, the functions of the non-neighbor samples are taken into consideration. Through application of the target function of the method, the neighbor relationships of the original neighbor samples are kept after projection; and the non-neighbor samples are kept away as far as possible after projection. With respect to solution of the target function, the dimensions of the samples are reduced to a non-zero space of an overhaul divergence matrix by employing a PCA algorithm, and then the target function is transformed into a difference form. The small sample problem is effectively solved. No any parameter needs to be set, and the practicability of the method is enhanced.

Description

technical field [0001] The invention belongs to the technical field of face recognition, and in particular relates to a face recognition method based on a mutual information non-parameter local preserving projection algorithm. Background technique [0002] Face recognition technology began in the mid-to-late 1960s and has been a research hotspot for many years. In recent years, it has become a popular research topic. Today's face recognition products have been widely used in finance, justice, military, public security, border inspection, government, aerospace, education, medical care and many enterprises and institutions. For the face recognition problem, because its sample dimension is very high, it is necessary to perform feature extraction (dimension reduction) processing on high-dimensional samples. Based on this, many feature extraction methods have been proposed, such as principal component analysis (Principal Component Analysis, PCA) and linear discriminant analysis ...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art, and provide a face recognition method based on the mutual information non-parameter local preservation projection algorithm, to solve the problem that the calculation method of the prior art cannot effectively maintain the local manifold of the sample, ignore non- The role of neighboring samples on classification and the problem of small samples in the objective function

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