Robust feature extracted by the generalized optimal discriminant vectors for face recognition

This paper introduces a novel Gabor direct generalized optimal discriminant vectors (GDG-ODV) method for face recognition (FR). This method can apply directly the generalized optimal discriminant vectors (G-ODV) method that views the optimal set of discriminant vectors as a global transform to the high-dimensional augmented Gabor feature vectors (AGFV) derived from the Gabor wavelet representation of face images. This method has three novelties: 1) it is robust to the facial variations; 2) it can efficiently solve the small sample size problem (SSSP), which widely exists in FR tasks; 3) it can consider the separability of optimal discriminant vectors (ODV) from a global viewpoint. The GDG-ODV method is compared, in terms of classification accuracy, to other common methods on the ORL database. The results indicate that this method is overall superior to those various FR methods.