High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Real-Time Face Detection
International Journal of Computer Vision
Experiments with an Improved Iris Segmentation Algorithm
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Iris Localization via Pulling and Pushing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
A study on eyelid localization considering image focus for iris recognition
Pattern Recognition Letters
UBIRIS: a noisy iris image database
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Robust iris verification based on local and global variations
EURASIP Journal on Advances in Signal Processing
Computer Vision and Image Understanding
Adaboost and multi-orientation 2D Gabor-based noisy iris recognition
Pattern Recognition Letters
Weighted co-occurrence phase histogram for iris recognition
Pattern Recognition Letters
Iris recognition in non-ideal imaging conditions
Pattern Recognition Letters
An iris recognition approach with SIFT descriptors
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Iris localization in frontal eye images for less constrained iris recognition systems
Digital Signal Processing
Gradual iris code construction from close-up eye video
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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Iris segmentation plays an important role in an accurate iris recognition system. In less constrained environments where iris images are captured at-a-distance and on-the-move, iris segmentation becomes much more difficult due to the effects of significant variation of eye position and size, eyebrows, eyelashes, glasses and contact lenses, and hair, together with illumination changes and varying focus condition. This paper contributes to robust and accurate iris segmentation in very noisy images. Our main contributions are as follows: (1) we propose a limbic boundary localization algorithm that combines K-Means clustering based on the gray-level co-occurrence histogram and an improved Hough transform, and, in possible failures, a complementary method that uses skin information; the best localization between this and the former is selected. (2) An upper eyelid detection approach is presented, which combines a parabolic integro-differential operator and a RANSAC (RANdom SAmple Consensus)-like technique that utilizes edgels detected by a one-dimensional edge detector. (3) A segmentation approach is presented that exploits various techniques and different image information, following the idea of focus of attention, which progressively detects the eye, localizes the limbic and then pupillary boundaries, locates the eyelids and removes the specular highlight. The proposed method was evaluated in the UBIRIS.v2 testing database by the NICE.I organizing committee. We were ranked #4 among all participants according to the evaluation results.