Analysis of the Parameters Involved in the Iris Recognition System

Biometric recognition is a computerized identification method which is based on unique features or characteristics possessed by human beings and Iris recognition has proved itself as one of the most reliable biometric methods available owing to the accuracy provided by its unique epigenetic patterns. The main steps in any iris recognition system are image acquisition, iris segmentation, iris normalization, feature extraction and features matching. EER (Equal Error Rate) metric is considered the best metric for evaluating an iris recognition system. In this paper, different parameters viz. the sigma for blurring with Gaussian filter while detecting edges, the scaling factor to fasten the CHT (Circle Hough Transform), the gamma correction factor for gamma correction and the radius for weak edge suppression for the edge detector during segmentation, the sigma upon central frequency and the central wavelength for convolving with Log-Gabor filter during feature extraction have been thoroughly tested and analyzed on the CASIA-IrisV1 database to get an optimized parameter set. This paper provides an insight into how the parameters must be set to have an improved Iris Recognition System.