By Topic

Half-Iris Feature Extraction and Recognition Using a New Class of Biorthogonal Triplet Half-Band Filter Bank and Flexible k-out-of-n:A Postclassifier

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Amol D. Rahulkar ; Department of Instrumentation Engineering, Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded, India ; Raghunath S. Holambe

Abstract-This paper presents a shift, scale, and rotation-in- variant technique for iris feature-representation and fused postclassification at the decision-level to improve the accuracy and speed of the iris-recognition system. Most of the iris-recognition systems are still incapable for providing low false rejections due to a wide variety of artifacts and are computationally inefficient. In order to address these problems, effective and computationally efficient iris features are extracted based on a new class of triplet half-band filter bank (THFB). First, a new class of THFB is designed by using generalized half-band polynomial suitable for iris feature extraction. This THFB satisfies perfect reconstruction (PR) and provides linear phase, regularity, better frequency-selectivity, near-orthogonality, and good time-frequency localization. The uses of these properties are investigated to approximate iris features significantly. Second, a novel flexible k-out-of-n.A (Accept) postclassifier (any k-out-of-n-regions-Accept) is explored to achieve the robustness against possible intraclass iris variations. The proposed approach (THFB+ k-out-of-n.A) is capable of handling various artifacts, particularly segmentation error, eyelid/eyelashes occlusion, shadow of eyelids, head-tilt, and specular reflections during iris verification. Experimental results using UBIRIS, MMU1, CASIA-IrisV3, and IITD databases show the superiority of the proposed approach with some of the existing popular iris-recognition algorithms.

Published in:

IEEE Transactions on Information Forensics and Security  (Volume:7 ,  Issue: 1 )
IEEE Biometrics Compendium
IEEE RFIC Virtual Journal
IEEE RFID Virtual Journal