Name
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). |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.imavis.2009.04.001 | Image Vision Comput. |
Keywords | DocType | Volume |
adaboost eye detection,iris detection error,iris recognition technique,iris image,color segmentation,new iris segmentation method,non-ideal iris images,non-ideal iris image,error rate,non-ideal quality iris image,iris region,iris segmentation method,specular reflection,false positive,visible light,iris recognition,edge detection | Journal | 28 |
Issue | ISSN | Citations |
2 | Image and Vision Computing | 33 |
PageRank | References | Authors |
1.20 | 11 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dae Sik Jeong | 1 | 151 | 7.98 |
| Jae Won Hwang | 2 | 33 | 1.20 |
| Byung Jun Kang | 3 | 281 | 14.98 |
| Kang Ryoung Park | 4 | 1325 | 104.82 |
| Chee Sun Won | 5 | 573 | 87.74 |
| Dong Kwon Park | 6 | 166 | 15.47 |
| Jaihie Kim | 7 | 1282 | 83.11 |