关键词: 人脸识别, 色彩信息, 二维主成分分析（2D-PCA）, 张量

Abstract: The color of facial images is one of the important features in face recognition, but the existing color face recognition based on 2D-PCA ignores its spatial relation. Therefore, a novel approach called Tensor PCA which uses a 3rd-order tensor to represent an RGB color image is proposed. In order to achieve the best classification, it seeks three projection matrices which consist of the eigenvectors corresponding to the largest eigenvalues of the n-mode total scatter matrix to maximize the distance of the projected samples, and constructs an ALS iterative procedure to optimize the projection matrices. As is shown in the results on Georgia Tech face database, in contrast with the process of 2D-PCA the recognition rate increases by 5.53% and the training time decreases by 78.1%.

Key words: face recognition, color information, two-Dimensional Principal Component Analysis（2D-PCA）, tensor