Robust Iris Segmentation Method Based on a New Active Contour Force with a Non-ideal Normalization

Mohammed A. M. Abdullah, Satnam S. Dlay, Wai Lok Woo, Jonathon A. Chambers

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)
20 Downloads (Pure)


Traditional iris segmentation methods give good results when the iris images are taken under ideal imaging conditions. However, the segmentation accuracy of an iris recognition system significantly influences its performance especially in nonideal iris images. This paper proposes a novel segmentation method for nonideal iris images. Two algorithms are proposed for pupil segmentation in iris images which are captured under visible and near infrared light. Then, a fusion of an expanding and a shrinking active contour is developed for iris segmentation by integrating a new pressure force to the active contour model. Thereafter, a noncircular iris normalization scheme is adopted to effectively unwrap the segmented iris. In addition, a novel method for closed eye detection is proposed. The proposed scheme is robust in finding the exact iris boundary and isolating the eyelids of the iris images. Experimental results on CASIA V4.0, MMU2, UBIRIS V1, and UBIRIS V2 iris databases indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris segmentation algorithms revealed considerable improvement in segmentation accuracy and recognition performance while being computationally more efficient.
Original languageEnglish
Pages (from-to)3128-3141
Number of pages14
JournalIEEE Transactions on Systems, Man and Cybernetics: Systems
Issue number12
Early online date19 May 2016
Publication statusPublished - Dec 2017
Externally publishedYes


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