Illumination-robust face recognition system based on differential components

Illumination variation generally causes performance degradation of face recognition systems under real-life environments. Therefore, we propose an illumination-robust face recognition system using a fusion approach based on efficient facial feature called differential two-dimensional principal component analysis (D2D-PCA) for consumer applications. In the proposed method, face images are divided into two sub-images to minimize illumination effects, and D2D-PCA is separately applied to each sub-images. The individual matching scores obtained from two sub-images are then integrated using a weighted-summation operation, and the fused-score is utilized to classify the unknown user. Performance evaluation of the proposed system was performed using an extended Yale face database B which consists of 2,414 face images for 38 subjects representing 64 illumination conditions under the frontal pose. Experimental results show that the proposed fusion approach enhanced recognition accuracy by 22.02% compared to that of 2DPCA, and we confirmed the effectiveness of the proposed face recognition system under illumination-variant environments.