Face Recognition by Estimating Facial Distinctive Information Distribution

Several studies of psychophysics have shown that the eyes or the mouth seem to be an important cue in human face perception, and the nose plays an insignificant role, this means that there exists a distinctive information distribution of faces.This paper presents a novel approach for face recognition by combining the Local Binary Patterns (LBP) based face descriptor and the distinctive information of faces. First, we give a quantitative estimation of the density for each pixel in fronted face image by combining Parzen-window approach and Scale Invariant Feature Transform (SIFT) detector, which is taken as the measure of the distinctive information of the faces. Second, we integral the density function in the sub-window region of face to gain the weights set which is used in the LBP based face descriptor to produce weighted Chi square statistics. As an elementary application of the estimation of distinctive information of face, the proposed method is tested on the FERET FA/FB image sets and yields a recognition rate of 98.2% contrast to the 97.3% which is produced by the method adopted by Ahonen.