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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Performance Evaluation of Local Descriptors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Effective Approach for Iris Recognition Using Phase-Based Image Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and accurate iris segmentation in very noisy iris images
Image and Vision Computing
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
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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This paper presents a weighted co-occurrence phase histogram (WCPH) for representing the local characteristics of texture pattern and applies it to iris recognition. We first introduce a weighting function that enables the phase angle of the image gradient at one pixel to contribute smoothly to several adjacent histogram bins. This accounts for the uncertainty of the phase angle estimation brought by disturbing factors such as noise and illumination changes. The weighting function also avoids the quantization problem typical of the traditional histogram. We then define the WCPH by computing the weighted co-occurrence of pairs of image pixels that are at fixed distance. The WCPH models the joint probability distribution of both the phase angle and spatial layout, thus having the potential to capture richer information in texture pattern. Based on the WCPH, we develop an iris recognition algorithm using the Bhattacharyya distance to measure the goodness of match. The recognition algorithm considers the effects of noise and employs a simple image registration scheme to account for image deformation. We evaluate the performance of the proposed work on the UBIRIS.v2 database. We participated in the Noisy Iris Challenge Evaluation-Part II (NICE:II). It evaluates the robustness to noise of iris encoding and matching methods on the UBIRIS.v2 database, where the iris images are captured at-the-distance and on-the-move. We ranked #5 among all the registered participants according to the evaluation of the NICE:II organizing committee.