Face Recognition Using Immune Network Based on Principal Component Analysis

This paper proposes a lace recognition method using artificial immune network classifiers 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. After this, these image data of reduced dimensions are input into an immune network to be trained. Subsequently the antibodies of the immune networks are optimized using genetic algorithms. The performance of the present method was evaluated using the AT&T Laboratories Cambridge database (formerly called ORL face database). The results show that this method gains higher recognition rate in contrast with some other methods.