Feature selection for subject identification in surveillance photos [face recognition applications]

In this paper, a novel face recognition method is proposed for surveillance photo identification applications. In such a case, only a limited number of images per subject is available for training purposes. Furthermore, surveillance photos are usually different from the stored templates mostly due to aging, illumination and pose variations. It is common practice to apply unsupervised techniques such as principle component analysis (PCA) when the sample size for each subject is small. However, since PCA is performed without sample label considerations, the captured variation between images contains not only interpersonal variation but also intrapersonal variation which has an adverse effect on recognition performance. To overcome the problem, feature selection is performed in the PCA space to obtain a representation in which intrapersonal variation is minimized and interpersonal variation is maximized. Extensive experimentation following the FERET evaluation protocol indicates that the proposed scheme improves significantly the recognition performance.