British Journal of Mathematics & Computer Science 11(5): 1-11, 2015, Article no.BJMCS.19490 ISSN: 2231-0851 SCIENCEDOMAIN international www.sciencedomain.org _____________________________________ *Corresponding author: Email: ronkebabs711@gmail.com, ronke.babatunde@kwasu.edu.ng; Local Binary Pattern and Ant Colony Optimization Based Feature Dimensionality Reduction Technique for Face Recognition Systems R. S. Babatunde 1* , S. O. Olabiyisi 2 , E. O. Omidiora 2 and R. A. Ganiyu 2 1 Department of Computer Science, College of Information and Communication Technology, Kwara State University, Malete, Nigeria. 2 Department of Computer Science and Engineering, Faculty of Engineering and Technology, Ladoke Akintola University of Technology, Ogbomoso, Nigeria. Article Information DOI: 10.9734/BJMCS/2015/19490 Editor(s): (1) H. M. Srivastava, Department of Mathematics and Statistics, University of Victoria, Canada. (2) Tian-Xiao He, Department of Mathematics and Computer Science, Illinois Wesleyan University, USA. Reviewers: (1) Anonymous, SPPU University, India. (2) Anonymous, Rajasthan Technical University, Kota, India. (3) Anonymous, University Center of FEI, Brazil. (4) Varun Shukla, Rajeev Gandhi Technical University, Bhopal, India. (5) Anonymous, Jawaharlal Nehru Technological University, Anantapur, India. Complete Peer review History: http://sciencedomain.org/review-history/11383 Received: 12 June 2015 Accepted: 16 August 2015 Published: 12 September 2015 _______________________________________________________________________________ Abstract Feature dimensionality reduction is the process of minimizing the number of features in high dimensional feature space. It encompasses two vital approaches: feature extraction and feature selection. In face recognition domain, widely adopted face dimensionality reduction techniques include Principal component analysis, Discrete wavelet transform, Linear discriminant analysis and Gabor filters. However, the performances of these techniques are limited by strict requirement of frontal face view, sensitivity to signal shift and sample size, computational intensiveness amongst others. In this paper, a feature dimensionality reduction technique that employed Local binary pattern for feature extraction and Ant colony optimization algorithms for the selection of optimal feature subsets was developed. The developed technique identified and selected the salient feature subsets capable of generating accurate recognition. The average training time, recognition time and recognition rate obtained from the experiment on locally acquired face data using cross-validation evaluation approach indicate an efficient performance of the potential combination of both methods in a two-level technique for dimensionality reduction. Original Research Article