Face recognition using combined multiple feature extraction based on Fourier-Mellin approach for single example image per person

At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only single example image per person. In order to deal with this problem of single example image per person is stored in the system in the real-world application. In this paper, we present a combined multiple features extraction based on Fourier-Mellin approach for face recognition with single example per person. The performance of Fourier-AFMT approach extracted frequency invariant features and OFMM approach extracted moment invariant features is applied individually, and these two kinds of features are combined and classified with correlation coefficient method (CCM). Experiments are implemented on YALE and ORL face databases to demonstrate the efficient of proposed methods. The experimental results show that the average recognition accuracy rate of our proposed methods higher than that of state-of-the-art methods.