Iris Recognition Performance Analysis for Noncooperative Conditions

A biometric system is presented using the human iris to help determine the authenticity of an individual. The system extracts the unique features of the iris that are recorded in templates. These templates are then compared with other irides utilising Daugman’s method. This follows a strict procedure (including segmentation, normalization, encoding and matching) over which a user has complete control. Often the recognition phase is crucial in nonoptimal or noncooperative conditions. In this work, a comparison is made of the relative accuracy of utilizing noisy iris datasets. The performance is analysed for a different number of iris images per person, for different number of individuals, for different noise levels using three different segmentations and three different encoding schemes. Adjustment of the Gabor filters’ bandwidth used in the encoding stage proves to be decisive in improving the accuracy for higher noise levels.