Swap Training: A Genetic Algorithm Based Feature Selection Method Applied on Face Recognition System

This paper presents a new feature selection method by modifying fitness function of genetic algorithm. Our implementation environment is a face recognition system which uses genetic algorithm for feature selection and k-Nearest Neighbor as a classifier together with our proposed Swap Training. In each iteration of genetic algorithm for assessment of one specific chromosome, swaps training switch the training and test data with each other. By using this method, genetic algorithm does not quickly converge to local minimums and final recognition rate will be enhanced. Obtained results from implementing the proposed technique on Yale Face database show performance improvement of genetic algorithm in selecting proper features.