Horizontal features based illumination normalization method for face recognition

This paper presents a novel filtering method for face recognition under varying illumination. The proposed method starts by normalizing the given input image by gamma transformation. The shadow artifacts in the normalized image are reduced with the decimation free directional filter banks (DDFB). We have used correlation coefficient as a similarity measure for face recognition. Empirically, we have proven that most of the discriminating features in a human face are horizontal in nature. The efficiency of the proposed method is evaluated on two public databases: Yale Face Database B, and the Extended Yale Face Database B. Experimental results demonstrate that the proposed method achieves higher recognition rate under varying illumination conditions in comparison with some other existing methods.