Teaching Learning Based Optimization (TLBO) Based Improved Iris Recognition System

Abstract

Optimization technique plays an major role in the iris system. The optimized feature gives an optimized template for matching process and these optimized template increases the identification rate of iris system. In this paper, we proposed Teaching Learning Based Optimization (TLBO) based iris recognition system in which feature extraction phase of iris recognition system is optimized by using TLBO. The process of feature extraction is performed by texture feature extraction Gabor wavelet transform technique. TLBO is than applied on these features. Teaching learning based optimization algorithm acquired the feature of iris image as a student and generates the optimized feature template as a teacher. The process of optimization designed the fitness constraints function for the selection of feature in student to teacher The proposed algorithm compared with other iris recognition methods Standard Iris Recognition System and Genetic Algorithm optimized Iris Recognition System. Experimental results when applied to CASIA dataset shows superior performance with better recognition rate.