A new iris segmentation method for non-ideal iris images

Dae Sik Jeong, Jae Won Hwang, Byung Jun Kang, Kang Ryoung Park, Chee Sun Won, Dong Kwon Park, Jaihie Kim

Research output: Contribution to journalArticlepeer-review

102 Citations (Scopus)


Many researchers have studied iris recognition techniques in unconstrained environments, where the probability of acquiring non-ideal iris images is very high due to off-angles, noise, blurring and occlusion by eyelashes, eyelids, glasses, and hair. Although there have been many iris segmentation methods, most focus primarily on the accurate detection with iris images which are captured in a closely controlled environment. This paper proposes a new iris segmentation method that can be used to accurately extract iris regions from non-ideal quality iris images. This research has following three novelties compared to previous works; firstly, the proposed method uses AdaBoost eye detection in order to compensate for the iris detection error caused by the two circular edge detection operations; secondly, it uses a color segmentation technique for detecting obstructions by the ghosting effects of visible light; and thirdly, if there is no extracted corneal specular reflection in the detected pupil and iris regions, the captured iris image is determined as a "closed eye" image. The proposed method has been tested using the UBIRIS.v2 database via NICE.I (Noisy Iris Challenge Evaluation - Part I) contest. The results show that FP (False Positive) error rate and FN (False Negative) error rate are 1.2% and 27.6%, respectively, from NICE.I report (the 5th highest rank).

Original languageEnglish
Pages (from-to)254-260
Number of pages7
JournalImage and Vision Computing
Issue number2
Publication statusPublished - 2010 Feb

Bibliographical note

Funding Information:
This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometrics Engineering Research Center (BERC) at Yonsei University [R112002105070020(2008)].

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Computer Vision and Pattern Recognition


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