Efficient iris segmentation using Grow-Cut algorithm for remotely acquired iris images

Chun Wei Tan, Ajay Kumar Pathak

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

14 Citations (Scopus)


This paper presents a computationally efficient iris segmentation approach for segmenting iris images acquired from at-a-distance and under less constrained imaging conditions. The proposed iris segmentation approach is developed based on the cellular automata which evolves using the Grow-Cut algorithm. The major advantage of the developed approach is its computational simplicity as compared to the prior iris segmentation approaches developed for the visible illumination iris segmentation images. The experimental results obtained from the three publicly available databases, i.e. UBIRIS.v2, FRGC and CASIA.v4-distance have respectively achieved average improvement of 34.8%, 31.5% and 31.4% in the average segmentation error, as compared to the recently proposed competing/best approaches. The experimental results presented in this paper clearly demonstrate the superiority of the developed iris segmentation approach, i.e., significant reduction in computational complexity while providing comparable segmentation performance, for the distantly acquired iris images.
Original languageEnglish
Title of host publication2012 IEEE 5th International Conference on Biometrics
Subtitle of host publicationTheory, Applications and Systems, BTAS 2012
Number of pages6
Publication statusPublished - 1 Dec 2012
Event2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012 - Arlington, VA, United States
Duration: 23 Sep 201227 Sep 2012


Conference2012 IEEE 5th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2012
Country/TerritoryUnited States
CityArlington, VA

ASJC Scopus subject areas

  • Computer Science Applications
  • Biomedical Engineering

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