Retina recognition system using adaptive neuro fuzzy inference system

Biometric based technologies including retina, face, fingerprint, speech, hand geometry, handwriting, iris and typing rhythm are used to deal high security problems because, they have reached a high degree of maturity such as applications on secure authentication. Artificial Neural Networks (ANN) are non-parametric prediction tools based on the analogy of biological nervous systems consisting of interconnected group of artificial neurons. They have information-processing units based on connectionist approach that can be used for a host of pattern classification and recognition applications. Fuzzy Logic (FL) is a powerful problem solving methodology receiving widespread acceptance for a range of applications. It provides a simple way to reach the definite conclusion from ambiguous, imprecise and vague information. Like ANN models, some Fuzzy Inference System (FIS) has the quality of universal approximation. ANN along with FL constitutes Adaptive Neuro Fuzzy Inference System (ANFIS) which can be used to model a system with non-linear, random and uncertain data. An ANFIS consists of the advantages of ANN and Fuzzy System (FS). In this paper, a biometric recognition system based on retina using ANFIS as classifier is described. A number of retina samples have been collected and used in this retina recognition system as inputs for proper identification. Here, Principal Component Analysis (PCA) is used for the feature extraction of the blood vessels of the retina. The specialty of the work is associated with the fact that if the ANFIS is configured properly in terms of number and types of membership functions (MFs), it can tackle the variations in the retinal images properly. Experimental results show that the system is reliable for such kind of identification system.