A REVIEW PAPER ON ON THE DESIGN OPTIMIZATION FOR ENHANCED FACE RECOGNITION

Face Recognition is one of the problems which can be handled very well using Hybrid techniques or mixed transform rather than single technique. This paper deals with using of Particle Swarm Optimization techniques for Face Recognition. 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 PCA [1] [2] Subspace using Accelerated Binary Particle Swarm Optimization. ABPSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. This paper proposes a novel method of Binary Particle Swarm Optimization called Accelerated Binary Particle Swarm Optimization (ABPSO) by intelligent acceleration of particles. Together with Image Pre-processing techniques such as Resolution Conversion, Histogram Equalization and Edge Detection, ABPSO is used for feature selection to obtain significantly reduced feature subset and improved recognition rate. The performance of ABPSO is established by computing the recognition rate and the number of selected features on ORL database. For the implementation of this propose work we use the Image Processing Toolbox under Matlab software