Face recognition using global and local Gabor features

Facial feature selection is one of the most important challenges in a face recognition system. In a face image, only a part of a face image is changed due to illumination, pose, and other source of changes. In this paper, a novel face recognition approach is proposed based on fusing global and local features. To extract global and local features, we employed Gabor wavelet filter to apply on whole image and non-overlapping sub-images with equal size. To reduce the dimension of new fused feature vector, Principal Component Analysis (PCA) technique is employed. In our experiments, we used KNN and multi-class SVM classifiers and ORL database to obtain face recognition rate. The results show that the new face recognition algorithm outperforms the conventional methods such as global Gabor face recognition, and G-2DFLD feature fusion face recognition in term of recognition rate.