Face Recognition Based on Wavelet Packet Coefficients and Radial Basis Function Neural Networks
- 1 Department of Computer Science, Virudhunagar Hindu Nadars’ Senthikumara Nadar College, Virudhunagar, India
- 2 Department of Computer Applications, Ayya Nadar Janaki Ammal College, Sivakasi, India
Abstract
An efficient face recognition system with face image representation using averaged wavelet packet coefficients, compact and meaningful feature vectors dimensional reduction and recognition using radial basis function (RBF) neural network is presented. The face images are decomposed by 2-level two-dimensional (2-D) wavelet packet transformation. The wavelet packet coefficients obtained from the wavelet packet transformation are averaged using two different proposed methods. In the first method, wavelet packet coefficients of individual samples of a class are averaged then decomposed. The wavelet packet coefficients of all the samples of a class are averaged in the second method. The averaged wavelet packet coefficients are recognized by a RBF network. The proposed work tested on three face databases such as Olivetti-Oracle Research Lab (ORL), Japanese Female Facial Expression (JAFFE) and Essexface database. The proposed methods result in dimensionality reduction, low computational complexity and provide better recognition rates. The computational complexity is low as the dimensionality of the input pattern is reduced.
- W. Zhao, R. Chellappa, A. Rosenfeld and P. J. Phillips, “Face Recognition: A Literature Survey,” Technical Report CAR-TR-948, University of Maryland, College Park, 2000.
- M. Turk and A. Pentland, “Eigenfaces for Recognition,” Cognitive Neuroscience, Vol. 3, No. 1, 1991, pp. 71-86. doi:10.1162/jocn.1991.3.1.71
- P. Belhumeur, J. Hespanha and D. Kriegman, “Eigenfaces vs Fisher Faces: Recognition Using Class Specific Linear Projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 7, 1997, pp. 711-720. doi:10.1109/34.598228
- D. L. Swets and J. Weng, “Using Discriminant Eigenfeatures for Image Retrieval,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 18, No. 8, 1996, pp. 831-836. doi:10.1109/34.531802
- J. Yang, D. Zhang, A. F. Frangi and J.-Y. Yang, “TwoDimensional PCA: A New Approach to AppearanceBased Face Representation and Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 1, 2004, pp. 131-138. doi:10.1109/TPAMI.2004.1261097
- A. N. Rajagopalan, K. S. Rao and Y. A. Kumar, “Face Recognition Using Multiple Facial Features,” Pattern Recognition Letters, Vol. 28, No. 3, 2007, pp. 335-341. doi:10.1016/j.patrec.2006.04.003
- B.-L. Zhang, H. H. Zhang and S. Z. S. Ge, “Face Recognition by Applying Wavelet Subband Representation and Kernel Associative Memory,” IEEE Transactions on Neural Networks, Vol. 15, No. 1, 2005, pp. 166-177. doi:10.1109/TNN.2003.820673
- C. Garcia, G. Zikos and G. Tziritas, “Wavelet Packet Analysis for Face Recognition,” Image and Vision Computing, Vol. 18, No. 4, 2000, pp. 289-297. doi:10.1016/S0262-8856(99)00056-6
- J. Z. Xue, H. Zhang and C. X. Zheng, “Wavelet Packet Transform for Feature Extraction of EEG during Mental Tasks,” Proceedings of the Second International Conference on Machine Learning and Cybernetics, Vol. 1, 2003, pp. 360-363.
- O. Boumbarov, S. Sokolov and G. Gluhchev, “Combined Face Recognition Using Wavelet Packets and Radial Basis Function Neural Network,” International Conference on Computer Systems and Technologies—CompSysTech’07, Bulgaria, 14-15 June 2007, pp. v.4.1-v.4.7.
- V. Perlibakas, “Face Recognition Using Principal Component Analysis and Wavelet Packet Decomposition,” Informatica, Vol. 15, No. 2, 2004, pp. 243-250.
- J.-T. Chien and C.-C. Wu, “Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition,” IEEE Transactions on Pattern analysis and Machine Intelligence, Vol. 24, No. 12, 2002, pp. 1644-1649. doi:10.1109/TPAMI.2002.1114855