Human-face identification method of local-keep mapping based on statistic non-relative and orthogoual characteristics

Abstract

The invention includes steps: first, carrying out principal component analysis for inputted training sample image so as to obtain projection matrix of principal component analysis; next, building a connection graph to obtain similarity between any two nodes, determining adjacent points of all nodes according to nearest neighbor principle, and calculating out similar matrix of inputted data; then, applying the similar matrix to method of local reservation mapping; adding two constrained conditions of statistical uncorrelation and orthogonal, based on eigenvalue problem combined with projection matrix of principal component analysis, the method determines incorrelate and orthogonal projection matrix so as to obtain training projection coefficient matrix, and testing projection coefficient matrix; finally, recognition is carried out by using method of minimum distance. Features are: minimal redundancy, in favor of reconstruction of raw data, applicable to identifying human face.

Description

technical field [0001] The invention relates to a method in the technical field of image processing, in particular to a face recognition method based on a statistically irrelevant and orthogonal characteristic of a part-preserving mapping. It can be used in various civil and military systems such as video surveillance systems, automatic door guard systems, and military target tracking and identification systems. Background technique [0002] Face recognition technology has become a hot research topic today. This technology has been successfully applied in identification, man-machine interface, automatic teller machine, video surveillance and other fields. At present, the difficulty of face recognition is mainly that the recognition accuracy is relatively low when the face changes in light, skin color, expression, posture, etc. [0003] As one of the key links of face recognition, the feature extraction method is to map the original high-dimensional data to a low-dimensiona...

AI Technical Summary

Problems solved by technology

However, the basis vectors of the projection matrix of the locality-preserving mapping method are statistically correlated and non-orthogonal, so the extracted features contain redundancy, overlapping information will cause the actual distribution of features to be distorted, and non-orthogonal features are not conducive to reconstruction of the original data, these two shortcomings seriously affect the performance of locality-preserving mapping algorithms

So far, no one has proposed a locality-preserving mapping method that satisfies both the statistically uncorrelated and orthogonal properties

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