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Face Recognition Using Biogeography Based Optimization

Author(s): Er. Navdeep Kaur Johal | Er. Poonam Gupta | Er. Amandeep Kaur

Journal: International Journal of Computer Science and Information Security
ISSN 1947-5500

Volume: 9;
Issue: 5;
Start page: 126;
Date: 2011;
Original page

Keywords: Face Recognition | Biogeography Based Optimization | DCT | Feature Selection

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 Biogeography Based Optimization (BBO). Biogeography-based optimization (BBO) is a recently-developed EA motivated by biogeography, which is the study of the distribution of species over time and space. The algorithm is applied to coefficients extracted by discrete cosine transforms (DCT). The proposed BBO-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 BBO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.
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