Abstract: The objective of face recognition involves the extraction of different features of the human face from the faceimage for discriminating it from other persons. It is the problem of searching a face in reference database to find thematches as a given face. The purpose is to find a face that has highest similarity with a given face in the database. Many face recognition algorithms have been developed and used as an application of access control and surveillance.This paper presents a novel feature selection algorithm based on Bacteria Foraging Optimization (BFO) and then compared the results with the ANN with respect to its accuracy in recognizing a face.BFOA has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real-world optimization problems arising in several application domains.In this paper face recognition is done by an efficient way using bacterial foraging optimization which is an optimization technique inspired from E. coli bacteria. Hence, this technique helps to reduce the computation complexity and time consuming.
Keywords: PCA, ANN,BFOA, rate of swim, rate of elimination