A Gabor direct fractional-step LDA algorithm for face recognition

Recently, a direct fractional-step linear discriminant analysis (DF-LDA) algorithm was proposed and successfully applied to face recognition (FR). However, the classification performance of DFT-LDA is degraded by the limitations of the direct linear discriminant analysis (D-LDA) used in DF-LDA. We describe a novel DF-LDA to solve this problem, and based on this novel DF-LDA, a novel Gabor DF-LDA (GDF-LDA), which directly applies the novel DF-LDA to the high-dimensional augmented Gabor feature vectors (AGFV) derived from the Gabor wavelet representation of face images, has been proposed for FR. The GDF-LDA not only is robust to facial variations, but also overcomes the limitations of the previous DF-LDA. The comparative results on the ORL database show that the GDF-LDA is more effective than existing FR methods.