Face recognition approach using Gabor Wavelets, PCA and SVM

Face recognition is an important research field of pattern recognition. Up to now, it caused researchers great concern from these fields, such as pattern recognition and computer vision. In general, we can make sure that the performance of face recognition system is determined by how to extract feature vector exactly and to classify them into a class correctly. Therefore, it is necessary for us to pay close attention to feature extractor and classifier. In this paper, we propose a methodological improvement to raise 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, followed by a face recognition algorithm, based on the principal component Analysis approach and Support Vector Machine (SVM) is used as a new classifier for pattern recognition. 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.