Robust Face Recognition from One Training Sample per Person

Abstract

This paper proposes a Gabor-based PCA method using Whiten Cosine Similarity Measure (WCSM) for Face Recognition from One training Sample per Person. Gabor wavelet representation of face images first derives desirable features, which is robust to the variations due to illumination, facial expression changes. PCA is then employed to reduce the dimensionality of the Gabor features. Whiten Cosine Similarity Measure is finally proposed for classification to integrate the virtues of the whiten translation and the cosine similarity measure. The effectiveness and robustness of the proposed method are successfully tested on CAS-PEAL dataset using one training sample per person, which contains 6609 frontal images of 1040 subjects. The performance enhancement power of the Gabor-based PCA feature and WCSM is shown in term of comparative performance against PCA feature, Mahalanobis distance and Euclidean distance. In particular, the proposed method achieves much higher accuracy than the standard Eigenface technique in our large-scale experiment.