Face recognition based on artificial immune networks and principal component analysis with single training image per person

Various methods could deal well with frontal view face recognition if there were sufficient number of representative training samples. However, few of them worked well if only single training image per person was available. This study proposes a face recognition method based on artificial immune networks and principal component analysis to solve the one training sample problem. The performance of the present method was evaluated utilizing the ORL face database. The results show that this method gains higher recognition rate in contrast with most of the developed methods.