Optimized human face recognition method and apparatus

Abstract

The invention discloses an optimized face recognition method and a device thereof, which improve the rate of facial image recognition. The technical proposal is that: the method and the device combine principal component analysis and linear discriminant analysis to solve the recognition problem; namely, the principal component analysis is carried out first before the conduction of the linear discriminant analysis so as to obtain relatively low dimensional space, then the space is utilized to carry out the linear discriminant analysis, and therefore, an intra-class dispersion matrix can not be caused and the process of the linear discrimination is effective. The invention firstly adopts the principal component analysis to obtain the best feature description, based on which, optimum identifying features are obtained by adopting the linear discriminant analysis, thereby greatly reducing the space dimension of facial features; finally, a minimum distance method is adopted to carry out classification and identification and therefore the rate of recognizing human faces is evidently increased. The method and the device of the invention are applied to face recognition.

Description

technical field [0001] The invention relates to the optimization of a face recognition algorithm, in particular to a face recognition method and device combining principal component analysis (PCA) and linear discriminant analysis (LDA) in face recognition. Background technique [0002] With the development of society and the advancement of technology, especially the rapid improvement of computer software and hardware performance in recent years, the requirements for fast and efficient automatic identity verification are increasingly urgent. Biometric technology has gained great attention and development in the field of scientific research. Because biological characteristics are the inherent attributes of human beings, they have strong self-stability and individual differences, so they are the most ideal basis for identity verification. Among them, the use of face features for identity verification is the most natural and direct means. Compared with other human biometric ide...

AI Technical Summary

Problems solved by technology

[0022] However, the problem encountered in practical application is that the dispersion matrix within the sample class is usually singular, because the number of samples of training samples is often smaller than the number of sample pixels contained in each sample, such as in the ORL face database, The face image has a pixel size of 112×92, which can be as high as 10304 dimensions after being converted into a vector representation, and the number of training samples is usually much smaller than this number. Therefore, under normal circumstances, face recognition always encounters a "small sample", resulting in the singularity of the intra-class scatter matrix, so that linear discriminant analysis cannot be directly used

Images