Face recognition using bacterial foraging strategy

This article presents an efficient algorithm for LDA-based face recognition with the selection of optimal principal components using E-coli Bacterial Foraging Optimization Technique. Different methods were suggested in the literature to select the largest eigenvalues and their corresponding eigenvectors for linear discriminant analysis (LDA). Some researchers have suggested eliminating the three largest eigenvalues to avoid the effect under varying illumination conditions. But, there is no unified approach for selecting optimal eigenvalues to enhance the performance of an algorithm. In this context, a GA-PCA algorithm has been proposed to select optimal eigenvalues and their corresponding eigenvectors in LDA. They proposed a fitness function to find the optimal eigenvectors using the Genetic Algorithm (GA). However, the crossover method used results in differences in offspring, and mutation never allowed them for a physical dispersal of the child in a chosen area. This prevents us in selecting optimal eigenvectors for improvising accuracy of the face recognition algorithm. This has motivated the authors to develop a new algorithm called BFO-Fisher which uses a nutrient concentration function (cost function) for optimization. In this work, the cost function is maximized through hill climbing via a type of biased random walk which is not possible in GA. Here the proposed BFO-Fisher algorithm offers us two distinct additional advantages (i) the proposed algorithm can supplement the features of GA, and (ii) the random bias incorporated into the BFO algorithm guides us to move in the direction of increasingly favorable environment, which is desirable. In this experiment, both Yale and UMIST Databases are used for the performance evaluation. Experimental results presented in this article reveal the fact that about 3% (Rank 1) improvement can be achieved as compared to the GA-Fisher algorithm. (C) 2011 Published by Elsevier Ltd