Background and Objective: Iris recognition is one of the popular winning biometric frameworks, giving promising outcomes in the identity authentication and access control systems. In this study, an efficient, fast and robust segmentation methodology suitable for non-cooperative and noisy iris images is proposed. Materials and Methods: This proposed methodology considers both shape and spatial feature properties of iris images taken from both the visible spectrum and near infrared spectrum. Circular hough transform is applied to the input image and iris outer boundary is identified. A minimum rectangular bounding box, MRB is defined using the obtained radius and center coordinates. High intensity valued, specular reflections and low intensity valued, pupil region, eyelids and eyelashes are identified using iterative thresholding and removed to reduce processing time. Scale invariant feature transform (SIFT) is directly applied on the segmented iris ROI, without performing normalization stage and system accuracy is tested. Results: By narrowing down the searching space to 65 times, this methodology provides robustness to noise as well as ensures faster segmentation of 0.34, 0.35 and 0.29 sec for CASIA V1.0, V3.0-interval and UBIRIS V1.0 datasets, respectively. Conclusions: The results obtained using improved segmentation methodology performs with improved recognition accuracy and reduced computational time and mislocalization count.
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