Improved Synthetic Iris Generation and Recognition Techniques

Abstract

Generation of synthetic iris is most widely in many applications. The iris is mostly used in identification of humans according to their features of human eyes.  Recent days large scale data is generating in many ways and many management systems use iris for enroll purposes. Many previously developed research methods try to synthesize the iris generation and check the whole iris image or texture. Various issues are identified with the previous existing approaches that consider cost and errors in generating the real data. Attacks can be made in many management systems. Many existing methods used to prevent the attacks in these systems. With the artificial identities the synthetic iris images can be created and also used for multiple purposes. Deep Learning algorithms perform well compare with compare with other algorithms to show the better iris recognition system. In this paper, the rapid CNN is developed to overcome the various issues in synthetic iris recognition system. Comparative results can be shown with traditional algorithms and proposed system.