A new iris segmentation method for non-ideal iris images

  • Authors:

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

  • Affiliations:

  • Dept. of Electronics Engineering, Biometrics Engineering Research Center (BERC), Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Republic of Korea;Dept. of Electronics Engineering, Biometrics Engineering Research Center (BERC), Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Republic of Korea;Biometrics Engineering Research Center (BERC), the Institute of Biomedical Eng., Hanyang University, 17 Haengdang-dong, Seongdong-gu, Seoul 133-791, Republic of Korea;Dept. of Electronics Engineering, Biometrics Engineering Research Center (BERC), Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Republic of Korea;Dept. of Electronics Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Republic of Korea;ImageproTech, Inc., Janghang-dong, Ilsandong-gu, Goyang-si, Gyeonggi-do 410-380, Republic of Korea;School of Electrical and Electronic Engineering, Biometrics Engineering Research Center (BERC), Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul 120-749, Republic of Korea

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2010

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Abstract

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).