Face Identification Using the magnitude and the phase of Gabor Wavelets and PCA

Face recognition has a wide range of possible applications in surveillance, access control, human computer interfaces and in electronic marketing and advertising for selected customers. Several models based on Gabor feature extraction have been proposed for face recognition with very good results on internationally available face databases. In this paper, we propose a methodological improvement to increase face recognition rate by fusing the phase and magnitude of Gabor's representations of the face as a new representation, in the place of the raster image. Although the Gabor representations were largely used, particularly in the algorithms based on global approaches, the Gabor phase was never exploited. We use a face recognition algorithm, based on the principal component Analysis approach. In the proposed algorithm, the global information is extracted using Eigenface. The resulted vector feature is classified using Euclidian distance. The performance of the proposed algorithm is tested on the public and largely used databases of FRGCv2 face and ORL databases. Experimental results on databases show that the combination of the magnitude with the phase of Gabor features can achieve promising results.