Face Image Feature Selection Based on Gabor Feature and Recursive Feature Elimination

Abstract

Face recognition is a research hotspot in recent years. In order to improve recognition accuracy of face recognition, a feature selection method for face image based on Gabor feature and recursive feature elimination was proposed in this paper. Firstly, Gabor features were extracted from face image. Then, face image was divided into pieces and Gabor feature statistics of these pieces were linked in series to compose the original face image feature. Finally, recursive feature elimination based feature selection method was used to construct a low dimensional face image feature for face recognition. The proposed method was verified on ORL face database and the extended Yale face database B, and got high recognition accuracies. The experimental results show that this method can accomplish face recognition satisfactorily and is not sensitive to the inconsistency of details, such as facial expressions, poses and illuminations.