Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns
International Journal of Computer Vision
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Method for the Identification of Inaccuracies in Pupil Segmentation
ARES '06 Proceedings of the First International Conference on Availability, Reliability and Security
Feature combination using boosting
Pattern Recognition Letters
Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Iris Spoof Detection via Boosted Local Binary Patterns
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ordinal Measures for Iris Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and accurate iris segmentation in very noisy iris images
Image and Vision Computing
Noise detection of iris image based on texture analysis
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Efficient iris recognition by characterizing key local variations
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Computer Vision and Image Understanding
The results of the NICE.II Iris biometrics competition
Pattern Recognition Letters
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
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In this paper, we present a noisy iris recognition frame which is learned by Adaboost on a 2D Gabor-based feature set. First, the irises are segmented and normalized by rubber sheet or simplified rubber sheet according to whether segmentations are accurate or not. Then, irises are divided into different amount of patches according to normalization. Moreover, a feature set is constructed based on 2D-Gabor for whole iris and patches. Finally, Adaboost learning is used for accurately and inaccurately segmented irises separately. The proposed method was evaluated by the NICE:II (Noisy Iris Challenge Evaluation - Part 2). We were ranked 2nd among all of the 67 participants from 29 different countries/districts.