PCA based immune networks for human face recognition

This paper proposes a face recognition method using artificial immune networks based on principal component analysis (PCA). The PCA abstracts principal eigenvectors of the image in order to get best feature description, hence to reduce the number of inputs of immune networks. Henceforth these image data of reduced dimensions are input into immune network classifiers to be trained. Subsequently the antibodies of the immune networks are optimized using genetic algorithms. The performance of the present method was evaluated employing the AT&T Laboratories Cambridge database. The results show that this method gains higher recognition rate in contrast with most of the developed methods.