Improved discriminant nearest feature space analysis for variable lighting face recognition

To improve the discriminant nearest feature space analysis (DNFSA) methods [6], in this paper, we propose an improved DNFSA (IDNFSA) algorithm to increase the robustness for variable lighting face recognition. The IDNFSA removes the mean of each image and attempts to minimize the within-class feature space (FS) distance and maximize the between-class FS distance simultaneously. In the IDNFSA, the first n eigenvectors are dropped and a generalized whitening transformation is suggested. In the recognition phase, the projected coefficients are classified by the nearest feature space rule with the ridge regression classification algorithm. Furthermore, to achieve higher accuracy, the illumination compensation is used. Experiments on the Extended Yale B (EYB) and FERET face databases reveal that the proposed approach outperforms the state-of-the-art methods for variable lighting face recognition.