Method for recognizing human face

Abstract

The invention discloses a method of face recognition. Firstly, known and classified face samples are trained for modeling via pairs of training modules to produce a support vector machine classifier capable of classifying and recognizing unknown face samples, then, features of the unknown face samples are extracted via recognition modules, and to-be-recognized recognition samples after being extracted out features are transferred to a trained support vector machine classifier to judge the faces which category belong to. The invention provides the method of face recognition based on nuclear stochastic mapping and support vector machines, and the method of nuclear stochastic mapping is initially used for extracting more effective face features in the process of extraction of the face features. In addition, the support vector machine classifier used for dichotomy classification is employed in the problem of face multiple classification, a pair of multi-strategies are employed to convert multi-problems into two-problems, and the invention not only can largely reduce feature dimensions for recognizing faces, but also can remarkably increase the recognition capability for the faces.

Description

technical field [0001] The invention belongs to the technical field of pattern recognition, in particular to a face recognition method, which is an algorithm for face feature extraction and recognition in the field of biological feature recognition. Background technique [0002] Biometric identification technology refers to the technology that uses the physiological characteristics or behavioral characteristics that human beings possess to identify their identity for identity verification. Compared with traditional identity verification technology, biometric technology fundamentally eliminates forgery and theft, has higher reliability and security, and has been more and more widely used in identity authentication of some security systems. [0003] As a typical biometric identification technology, face recognition technology has been favored by people for its naturalness and high acceptability, and has broad applications in national public security, judicial fields, financial...

AI Technical Summary

Problems solved by technology

[0004] Traditional face recognition methods such as principal component analysis (reference [1]: M.Turk and A.Pentland, "Eigen faces for recognition," Journal of Cognitive Neuroscience, vol.3, no.1, 1991, pp. 71-86), linear discriminant methods (reference [2]: P.N.Belhumeur, J.P Hespanha, and D.J.Kriegman, "Eigenfaces Vs fisherfaces: Recognition using class specific linear projection," IEEE Trans on Patt.Anal.and Machine Intell.vol. 19, no.7, 1997, pp.711-720) Although it can effectively reduce the dimensionality of high-dimensional face features and achieve good recognition results, it ignores the differences between different samples or cannot solve nonlinear features question

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