Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. The algorithm is applied to coefficients extracted by two feature extraction techniques: the discrete cosine transforms (DCT) and the discrete wavelet transform (DWT). The proposed PSO-based feature selection algorithm is utilized to search the feature space for the optimal feature subset where features are carefully selected according to a well defined discrimination criterion. Evolution is driven by a fitness function defined in terms of maximizing the class separation (scatter index). The classifier performance and the length of selected feature vector are considered for performance evaluation using the ORL face database. Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.

1. W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, vol. 35, no. 4, pp. 399-458, 2003.
2. R. Brunelli and T. Poggio, "Face Recognition: Features versus Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042- 052, 1993.
3. C. Liu and H. Wechsler, "Evolutionary Pursuit and Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 6, pp. 570-582, 2000.
4. M. A. Turk and A. P. Pentland, "Face Recognition using Eigenfaces," Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-591, June 1991.
5. L. Du, Z. Jia, and L. Xue, "Human Face Recognition Based on Principal Component Analysis and Particle Swarm Optimization -BP Neural Network," Proc 3rd Conference. on Natural Computation (ICNC 2007),vol. 3, pp. 287-291, August 2007.
6. X. Yi-qiong, L. Bi-cheng and W. Bo, "Face Recognition by Fast Independent Component Analysis and Genetic Algorithm," Proc. of the 4th International Conference on Computer and Information Technology (CIT'04), pp. 194-198, Sept. 2004.
7. X. Fan and B. Verma, "Face recognition: a new feature selection and classification technique," Proc. 7th Asia-Pacific Conference on Complex Systems, December 2004.

Discrete Cosine Transform Discrete Wavelet Transform Face Recognition Feature Selection Genetic Algorithm Particle Swarm Optimization