关键词: 人脸识别, 降维, 流形学习, 张量, 子空间

Abstract:

The current algorithms based on tensor subspace manifold learning can utilize the intrinsic geometrical structure of images. But the local information and global information are not utilized sufficiently in current algorithms. A novel tensor subspace learning algorithm is proposed in this paper which is named tensor local and global projection. The local nonlinear structure of the data manifold that is the local information of the data can be preserved in the algorithm, and at the same time, the global information of data is utilized. So the discriminant between classes of data in low dimension subspace can be maximized. And the optimal tensor subspace can be obtained by iteratively computing the generalized eigenvectors and projection. The experiments on the standard face database demonstrate that the right recognition rate of the novel algorithm is higher than the recognition rate of the four algorithms named TLDA,TMFA,TLDP and TSA.

Key words: face recognition, dimensional reduction, manifold learning, tensor, subspace