A robust feature extraction framework for face recognition

Guang Dai, Yasutoshi Otani

Abstract: The kernel fractional-stop nonlinear discriminant analysis (KF-NDA) method not only extends the fractional-step linear discriminant analysis (F-LDA) method to a nonlinear version, but also further improves the generalization ability of traditional kernel nonlinear discriminant analysis (K-NDA). On the other hand, the Gabor transformed face images exhibit strong characteristics of spatial locality, scale and orientation selectivity, similar to those displayed by Gabor wavelets. Such characteristics produce salient local features that are most suitable for face recognition (FR). Hence, the augmented Gabor feature vector (AGFV) derived from a set of downsampled Gabor wavelet representations of face images is robust to the various of face images and simultaneously exhibits the more discriminatory information. Based on the AGFV and the KF-NDA, a robust feature extraction framework, i.e., the Gabor KF-NDA (GKF-NDA), is proposed for FR. In this framework, the KF-NDA method is directly applied to extract the robust nonlinear feature from the AGFV. Experimental results tested on the popular databases show that the GKF-NDA is more effective than oilier existing FR approaches.