Fusing multiple features for Fourier Mellin-based face recognition with 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, a comparative study of the AFMT, Fourier-AFMT and Taylor-AFMT are identified for face recognition. And then, we present hybrid Fourier-AFMT framework based face recognition for feature extraction. Firstly, both directionality of edges and intensity facial features are extracted and secondly fuse two kinds of features and classify 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 rates of our proposed fuse multiple feature domains much higher than that of single feature domain.
face recognition single example image per person gradient direction wavelet transform fourier transform analytical fourier-mellin transform (afmt) taylor invariant