Face recognition based on histogram of the 2D-FrFT magnitude and phase

In face recognition, there are great challenges with variations arising from illumination, expression and other factors. Since the fractional Fourier transform feature is robust to illumination and expression variations and has been used in face recognition area, we propose a novel algorithm to face recognition with the local region histogram of the two dimensional fractional Fourier transform (2D-FrFT) magnitude and phase (LFMP). A face image is modeled as a “histogram sequence” by concatenating the histogram of all local regions of 2D-FrFT magnitude and phase binary pattern maps. The histogram intersection is used to measure the similarity of different LFMP binary pattern maps and the nearest neighborhood is exploited for final classification. We evaluate our approach on ORL and FERET face databases. Extensive experimental results verify the effectiveness of our LFMP descriptor.