Select eigenfaces for face recognition with one training sample per subject

In many real applications for face recognition, such as surveillance photo identification, each subject only has one image sample for training which makes many supervised learning techniques fail to apply. Furthermore, since subject appearance has large variabilities due to aging, illumination and camera viewpoints, the face images to be identified are usually different from the stored templates. In this paper, a novel solution to this problem is proposed based on the well known unsupervised methodology, eigenface. We proposed a criterion to select the eigenfaces forming a feature subspace in which the intrapersonal variation is small compared to interpersonal variation and as well as most discriminating power is retained. The selection criterion maximizes the ratio between inter and intra personal variation, and at the same time takes total inter variation into account. Extensive experimentation following the FERET evaluation protocol indicates that the proposed scheme improves significantly the recognition performance.