Iris recognition: A symbolic data modeling approach using Savitzky-Golay filter energy features

Abstract

This paper presents a novel approach for iris recognition through symbolic modeling and symbolic similarity analysis of iris features, which are transformed using Savitzky-Golay filter. The proposed approach is based on iris Savitzky-Golay filter energy features. Prior to symbolic modeling of iris feature data, Canny edge detector and Hough Transform technique is used to segment iris region from the eye image. In the normalization stage, Daugman's rubber sheet model is used to obtain a fixed size rectangular block from segmented iris region to account for imaging inconsistencies. Using Savitzky-Golay filter, iris features are obtained by computing smoothing coefficients from normalized iris region. Further, the extracted features are represented as symbolic object by employing symbolic data modeling approach. The symbolic similarity analysis technique is employed for computing similarity between probe iris image and symbolic objects. The symbolic object which represents maximum similarity is considered as a resultant object. The SGGSIE&T and CASIA-Iris 4.0 Interval databases were selected to evaluate the performance of proposed model and achieved 99.33% and 95% recognition rates respectively.