An Implementation of FACE Recognition System (FARS) Using PCA and PSO Based Techniques
Mukesh Tiwari1, Arun Kumar Shukla2
1Mukesh Tiwari, Shepherd School of Engineering and Technology, Sam Higginbottom Institute of Agricultural Technology and Sciences, Allahabad (U.P), India.
2Arun Kumar Shukla, Shepherd School of Engineering and Technology, Sam Higginbottom Institute of Agricultural Technology and Sciences, Allahabad (U.P), India.
Manuscript received on 13 August 2016 | Revised Manuscript received on 20 August 2016 | Manuscript Published on 30 August 2016 | PP: 225-229 | Volume-5 Issue-6, August 2016 | Retrieval Number: F4726085616/16©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Feature selection (FS) is a universal 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. Feature selection aims to choose a small number of relevant features to achieve similar or even better classification performance than using all features. It has two main conflicting objectives of maximizing the classification performance and minimizing the number of features. However, most existing feature selection algorithms treat the task as a single objective problem. In this paper we present a novel feature selection system, FARS, based on combination of particle swarm optimization (PSO) and Principle Component Analysis (PCA). The proposed PSO and PCA based feature selection system is utilized to search the feature space for the optimal feature subset where features are carefully selected according to a well defined discrimination criterion. The classifier performance and the length of selected feature vector are considered for performance evaluation using MATLAB in ORL face database.
Keywords: Face Recognition, Feature selection, PSO, PCA, ORL Dataset
Scope of the Article: Pattern Recognition