A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
Iris Localization via Pulling and Pushing
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Novel and Fast Approach for Iris Location
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 01
Robust iris location in close-up images of the eye
Pattern Analysis & Applications
A study on eyelid localization considering image focus for iris recognition
Pattern Recognition Letters
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient iris segmentation method for recognition
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Study and improvement of iris location algorithm
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
An accurate and fast iris location method based on the features of human eyes
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Circuits and Systems for Video Technology
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Iris recognition is a reliable and accurate biometric technique used in modern personnel identification system. Segmentation of the effective iris region is the base of iris feature encoding and recognition. In this paper, a novel method is presented for fast iris segmentation. There are two steps to finish the iris segmentation. The first step is iris location, which is based on rotation average analysis of intensity-inversed image and non-linear circular regression. The second step is eyelid detection. A new method to detect the eyelids utilizing a simplified mathematical model of arc with three free parameters is implemented for quick fitting. Comparatively, the conventional model with four parameters is less optimal. Experiments were carried out on both self-collected images and CASIA database. The results show that our method is fast and robust in segmenting the effective iris region with high tolerance of noise and scaling.