Fusing 2DKPCA and 2D(PC)2A for Image Matrix Based Face Recognition with One Training Sample Per Person

In the face recognition area, a so-called one sample per person problem occurred owing to the difficulties of collecting samples or storage space of systems. In this paper, we present a unified framework for image matrix based face recognition with one training sample per person. Firstly, the nonlinear and linear facial features are using proposed 2DKPCA and 2D(PC)²A method, the face images are directly used for feature extraction, and secondly a parallel fusion method is applied to fuse the facial features to construct the combined features. Experiments are implemented on three face databases to demonstrate the feasibility of proposed algorithm.