Face Recognition Based on SVM and GABOR Filter

Abstract

In this paper, Support Vector machines (SVM)-based face recognition system is proposed. Here we used Gabor filter coefficients as features describing face images. Considering the desirable characteristics of spatial frequency and orientation selectivity of the Gabor filter, we design filter for extracting facial features from the face image. The feature vector based on Gabor filters is used as the input to the SVM classifier. The system has been evaluated on Yale face database-B. To reduce the computational complexity and memory consumption, the images are resized to 27×18 jpg format. Homomorphic filtering is used as a preprocessing operation. After preprocessing, the image is convolved with Gabor filters by multiplying the image by Gabor filters in frequency domain to obtain the Gabor features. These features are given to the SVM classifier for training and testing purpose. The results show that this method is the fastest one, having approximately 100% recognition rate.