Abstract—This paper presents a novel face recognition method based on the Gabor filter bank, Kernel Principle Component Analysis (KPCA) and Support Vector Machine (SVM). At first, the Gabor filter bank with 5 frequencies and 8 orientations is applied on each face image to extract robust features against local distortions caused by variance of illumination, facial expression and pose. Then, the feature reduction technique of KPCA is performed on the outputs of the filter bank to form the new low-dimensional feature vectors. Finally, SVM is used for classification of the extracted features. The proposed method is tested on the ORL face database. The experimental results reveal that the proposed method has a maximum recognition rate of 98.5% which is higher than the other related algorithms applied on the ORL database.
Index Terms—Face recognition, Gabor filter bank, kernel principle component analysis, support vector machine.
Saeed Meshgini and Hadi Seyedarabi are with the Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran (e-mail: email@example.com, firstname.lastname@example.org).
Ali Aghagolzadeh was with the Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. He is now with the Faculty of Electrical and Computer Engineering, Babol Nooshirvani University of Technology, Babol, Iran (e-mail: email@example.com).
Cite: Saeed Meshgini, Ali Aghagolzadeh, and Hadi Seyedarabi, "Face Recognition Using Gabor Filter Bank, Kernel Principle Component Analysis and Support Vector Machine," International Journal of Computer Theory and Engineering vol. 4, no. 5, pp. 767-771, 2012.