A REVIEW PAPER ON AN ENHANCED FACE RECOGNITION SYSTEM USING CORRELATION METHOD AND ABPSO
Journal Title: International Journal of Engineering Sciences & Research Technology - Year 30, Vol 5, Issue 6
Abstract
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, whichreduces the number of features, removes irrelevant, noisy and redundant data, and results inacceptable recognition accuracy. It is the most important step that affects the performance ofa pattern recognition system. This paper presents a novel feature selection algorithm basedon PCA [1] [2] Subspace using Accelerated Binary Particle Swarm Optimization. ABPSO is a computational paradigm based on the ideaof collaborative behavior inspired by the social behavior of bird flocking or fish schooling. This paper proposes a novel method of Binary Particle Swarm ptimization called Accelerated Binary Particle Swarm Optimization (ABPSO) by intelligent acceleration of particles. Together with Image Preprocessing techniques such as Resolution Conversion, Histogram Equalization and Edge Detection, ABPSO is used for feature selection to obtain significantly reducedfeature 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 proposed work we use the Image Processing Toolbox under Matlab software.
Authors and Affiliations
Ms. Navpreet Kaur
Keywords
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- EP ID EP143889
- DOI 10.5281/zenodo.55973
- Views 49
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