Digital Image Processing
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reconfigurable Media Processing
ITCC '01 Proceedings of the International Conference on Information Technology: Coding and Computing
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
An improved method for Daugman's iris localization algorithm
Computers in Biology and Medicine
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier
Expert Systems with Applications: An International Journal
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
IEEE Transactions on Pattern Analysis and Machine Intelligence
A novel iris segmentation using radial-suppression edge detection
Signal Processing
Iris image segmentation and sub-optimal images
Image and Vision Computing
Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition
Image and Vision Computing
Reliable algorithm for iris segmentation in eye image
Image and Vision Computing
A knowledge-based approach to the iris segmentation problem
Image and Vision Computing
Robust and accurate iris segmentation in very noisy iris images
Image and Vision Computing
A new iris segmentation method for non-ideal iris images
Image and Vision Computing
Noisy iris segmentation with boundary regularization and reflections removal
Image and Vision Computing
A highly accurate and computationally efficient approach for unconstrained iris segmentation
Image and Vision Computing
Feature correlation evaluation approach for iris feature quality measure
Signal Processing
The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iris segmentation in non-ideal images using graph cuts
Image and Vision Computing
A Robust Iris Localization Method Using an Active Contour Model and Hough Transform
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Robust iris verification based on local and global variations
EURASIP Journal on Advances in Signal Processing
Unconstrained iris acquisition and recognition using COTS PTZ camera
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Iris recognition using artificial neural networks
Expert Systems with Applications: An International Journal
A robust iris segmentation with fuzzy supports
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Rough set approach to online signature identification
Digital Signal Processing
Face recognition using difference vector plus KPCA
Digital Signal Processing
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 0.00 |
Commercial iris recognition systems do not perform well for non-ideal data, because their iris localization algorithms are specifically developed for controlled data. This paper presents a robust iris localization algorithm for less constrained data. It includes: (i) suppressing specular reflections; (ii) localizing the iris inner (pupil circle) and outer (iris circle) boundaries in a two-phase strategy. In the first phase, we use Hough transform, gray level statistics, adaptive thresholding, and a geometrical transform to extract the pupil circle in a sub-image containing a coarse pupil region. After that, we localize iris circle in a sub-image centered at the pupil circle. However, if the first phase fails, the second phase starts, where first we localize a coarse iris region in the eye image. Next, we extract pupil circle within the coarse iris region by reusing procedure of first phase. Following that, we localize iris circle. In either of the two phases, we validate the pupil location by using an effective occlusion transform; and (iii) regularizing the iris circular boundaries by using radial gradients and the active contours. Experimental results show that the proposed technique is tolerant to off-axis eye images, specular reflections, non-uniform illumination; glasses, contact lens, hair, eyelashes, and eyelids occlusions.