Robust Face Recognition from a Single Training Image per Person with Kernel-Based SOM-Face

Xiaoyang Tan, Songcan Chen, Zhi-Hua Zhou, Fuyan Zhang

Abstract: In this paper, a kernel-based SOM-face method is proposed to recognize expression variant faces under the situation of only one training image per person. Based on the localization of the face, an unsupervised kernel-SOM learning procedure is carried out to capture the common local features and the non-Euclidean structure of the image data, so that a compact and robust representation of the face can be obtained. Experiments on the FERET face database show that the Kernel-based SOM-face method can obtain higher recognition performance than the regular SOM-face method.