Features Selection Based on Modified PSO Algorithm for 2D Face Recognition

In this paper, we propose a technique of features selection based on modified particle swarm optimization (MPSO) for face recognition system. PSO is a new class of algorithm for feature selection based on the idea of collaborative behavior of bird flocking. The feature selected by the proposed MPSO algorithm plays a vital role to search the solution space for an optimum solution where features are carefully selected according to a well defined discrimination criterion. Several novelties are introduced to make the recognition robust to varying illumination, facial expressions and poses at certain angles is challenging. Image of the face is divided first into sub-regions. Afterwards, the MPSO algorithm is applied to coefficients extracted by Discrete Wavelet Transform (DWT). We illustrate the experimental results of our new algorithm with the minimal set of selected features using different experimental protocols on several databases, including Yale Face, FEI and ORL.