基于张量局部和全局信息的人脸识别算法

现有的基于张量子空间的流形学习算法能够很好地利用图像的空间几何结构，但对流形的局部和全局信息利用得不够充分，为此提出了一种新的张量子空间学习算法:基于局部和全局信息的张量子空间投影．新算法充分利用人脸图像数据的局部流形结构(即类内非线性流形结构)和人脸图像数据的全局信息，使数据在投影空间中的类间分离度最大，通过迭代和投影得到最优张量子空间．在标准人脸数据库上的实验表明，新算法识别率高于张量线性判别分析(TLDA)、张量临界Fisher分析(TMFA)、张量局部判别投影(TLDP)、张量子空间(TSA)算法．

New face recognition algorithm using tensor local and global information

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.