Two-dimensional signal and image processing
Two-dimensional signal and image processing
The Quotient Image: Class-Based Re-Rendering and Recognition with Varying Illuminations
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable and Fast Eye Finding in Close-up Images
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Experiments with an Improved Iris Segmentation Algorithm
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Off-Angle Iris Recognition Using Bi-Orthogonal Wavelet Network System
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
A Robust and Accurate Segmentation of Iris Images Using Optimal Partitioning
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Face recognition under varying lighting conditions using self quotient image
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Localized iris image quality using 2-d wavelets
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A novel iris segmentation method for hand-held capture device
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Iris recognition at a distance
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Circuits and Systems for Video Technology
Speed-up multi-stage non-cooperative iris recognition
International Journal of Biometrics
Effective elliptic fitting for iris normalization
Computer Vision and Image Understanding
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Iris biometric is one of the most reliable biometrics with respect to performance. However, this reliability is a function of the ideality of the data. One of the most important steps in processing nonideal data is reliable and precise segmentation of the iris pattern from remaining background. In this paper, a segmentation methodology that aims at compensating various nonidealities contained in iris images during segmentation is proposed. The virtue of this methodology lies in its capability to reliably segment nonideal imagery that is simultaneously affected with such factors as specular reflection, blur, lighting variation, occlusion, and off-angle images.We demonstrate the robustness of our segmentation methodology by evaluating ideal and nonideal data sets, namely, the Chinese Academy of Sciences iris data version 3 interval subdirectory, the iris challenge evaluation data, theWest Virginia University (WVU) data, and theWVU off-angle data. Furthermore, we compare our performance to that of our implementation of Camus and Wildes's algorithm and Masek's algorithm. We demonstrate considerable improvement in segmentation performance over the formerly mentioned algorithms.