Face Identification System by merging Reflectance and Illumination features

among the numerous biometric systems existing in the literature, face identification systems have received a considerable interest in latest years. This paper presents a novel approach to face-feature extraction based on the Adaptive Single scale Retinex algorithm (ASSR) and the Gabor filter-bank. The ASSR has been used to extract the illumination (I-image) and the reflectance images (R-image) from each original face image. NIimage (normalization illumination image) has been obtained by eliminating the uneven lighting from the I-image using morphological operations. Then, the Gabor filter bank is applied on the NI-image and the reflectance images to extract feature vectors of these images. These features have been concatenated to make a huge feature vector of every user. While in the next step PCA + LDA technique has been employed to reduce the dimensionality of these novel feature vectors of every user and to further improve its discriminatory power. Finally, the nearest neighbor classifier with cosine Mahalanobis distance has been used for matching and decision stages respectively. Experimental results demonstrate that the proposed system reaches better results than the existing in the state-of-the-art systems.