Particle Swarm Optimization- Best Feature Selection method for Face Images
P. V. Shinde, Baisa Laxman Gunjal · 2012
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 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. � Formulation of a new feature selection algorithm for face recognition based on the binary PSO algorithm. The algorithm is applied DWT feature vectors and is used to search for the optimal feature subset to increase recognition rate and class separation. � Evaluation of the proposed algorithm using the ORL face database and comparing its performance with a PCA, ICA LDA feature selection algorithm and various FR algorithms found in the literature.