Face recognition method based on kernel nearest subspace

Abstract

The invention discloses a face recognition method based on kernel nearest subspace, mainly solving the problem that the non-linear characteristics of the data can not be subjected to linear expression in the existing methods. The method comprises the following steps: (1) mapping the training sample matrixes and the testing samples to the non-linear characteristic space by Mercer kernel experience, then carrying out dimension reduction and normalization on the mapped samples and then extracting each class of training samples undergoing dimension reduction; (2) solving the sample reconstruction coefficient between the normalized testing samples and each class of training sample matrixes and reconstructing the original testing samples; and (3) obtaining the residual errors between various classes of reconstructed samples and the original testing samples and taking the class of the subscript corresponding to the minimum in the residual errors as the class of the testing samples. The method improves the precision in face recognition application, simultaneously expands the application range to the low-dimensional samples so as to further have universality and can be used for supervision and protection of public security, information security and financial security.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to pattern recognition, in particular to a face recognition method, which can be used for supervision and protection of public security, information security and financial security. Background technique [0002] As one of the key technologies of biometric identification, face recognition technology has potential application prospects in public security, information security, finance and other fields. Human faces are generally considered to be the most researchable objects in the field of image recognition. On the one hand, this is because the human face has a remarkable recognition ability in the human visual system, and on the other hand, it is because there are a large number of important applications in automatic face recognition technology. In addition, technical problems in face recognition also cover the problems encountered in pattern recognition research. Since the f...

AI Technical Summary

Problems solved by technology

However, the nearest subspace classifier proposed by Kuang-Chih Lee, Jeffrey Ho, and David Kriegman is only a linear representation of the original features of the data, and cannot linearly represent the nonlinear features of the data, and its globality is for a certain type of data. It is not the overall data set, so the classifier has low recognition accuracy for face data with nonlinear features

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