Iris feature extraction and matching based on multiscale and directional image representation

Chul Hyun Park, Joon Jae Lee, Sang Keun Oh, Young Chul Song, Doo Hyun Choi, Kil Houm Park

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

14 Scopus citations

Abstract

This paper presents a new filterbank-based iris recognition method that effectively extracts the spatial and directional features of iris patterns on multiple scales, then performs matching. First, the proposed method localizes the iris area from an input image and establishes a region of interest (ROI) for feature extraction. Second, the iris features are extracted on multiple scales from the ROI and a feature vector generated using a band pass filter and directional filter bank (DFB), which decomposes the image into several directional subband outputs. Finally, iris pattern matching robust to various rotations of the input is performed based on finding the Hamming distance between the corresponding feature vectors. Experimental results demonstrate that the proposed method is both effective in extracting directional and multiresolutional features from iris patterns and robust to input image rotation due to head tilt.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsLewis D. Griffin, Martin Lillholm
PublisherSpringer Verlag
Pages576-583
Number of pages8
ISBN (Print)354040368X
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2695
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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