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, andother 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-imageswith 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 algorithmoutperforms the conventional methods such as global Gabor face recognition, and G-2DFLD feature fusion face recognition in term of recognition rate

کلیدواژه ها:

Facial feature extraction ، Gabor wavelet ، Global feature ، Local feature ، PCA