A Hybird Particle Swarm/Nelder-Mead Clustering Algorithm for Face Recognition

This paper presents a face recognition system that uses a hybrid clustering algorithm. The proposed clustering algorithm combines Particle Swarm Optimization (PSO) and Nelder-Mead nsearch method. The Nelder-Mead search scheme is implemented to improve the search process in the PSO algorithm. The proposed system starts by computing the Eigenfaces for all faces in the database and then these Eigenfaces are used to compute the Eigen weight vectors as feature vectors. Next, PSO algorithm is applied to group the input feature vectors into clusters. The Nelder Mead search is integrated in the PSO clustering algorithm to efficiently converge to a global optimum solution. Finally, the matching distances between the input face and the predefined clusters are detected, the minimum distance indicates the input sample cluster. A set of experiment is conducted on two datasets, Indian database and European database. The results show that the proposed scheme outperforms the recognition performance of the Eigenface solution and improves significantly the K-means clustering algorithm.