Face Recognition Based on Mixed between Selected Feature by Multiwavelet and Particle Swarm Optimization
Adil Abdulwahhab Ghidan Azzawi, Muneera Alsaedi · 2010
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 wavelet transform (DMWT). 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. In this paper, a face recognition algorithm using a PSO-based feature selection approach is presented. The algorithm utilizes a novel approach that employs the binary PSO algorithm to effectively explore the solution space for the optimal feature subset. The selection algorithms applied to feature vectors extracted using the DMWT. The search heuristics in PSO is iteratively adjusted guided by a fitness function defined in terms of maximizing class separation. The proposed algorithm was found to generate excellent recognition results witless selected features then the main contribution of this work: The first contribution is formulation of a new feature selection algorithm for face recognition based on the binary PSO algorithm. The algorithm is applied to DMWT feature vectors and is used to search for the optimal feature subset to increase recognition rate and class separation. Then the second contribution is evaluation of the proposed algorithm using the ORL face database and comparing its performance with a GA-based feature selection algorithm and various FR algorithms found in the literature. The rest of this paper is organized as follows. The DMWT feature extraction techniques are described in Section 2. An overview of Particle Swarm Optimization (PSO) is presented in Section 3. In Section 4 we explain the proposed PSO-based feature selection algorithm. Finally, Sections 5 and 6 attain the experimental results and conclusion.