Iris feature extraction using principally rotated complex wavelet filters (PR-CWF)

Charles O. Ukpai, S. S. Dlay, W. L. Woo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Deriving effective iris feature from the segmented iris image is a crucial step in iris recognition system. In this paper we propose a new iris feature extraction method based on the Principal Texture Pattern (PTP) and dual tree complex wavelet transform (DT-CWT). We compute the principal direction (PD) of the iris texture using principal component analysis (PCA) and obtain the angle θ of the PD. Then, complex wavelet filters CWFs are constructed and rotated in the direction θ of the PD and also in the opposite direction - θ for decomposition of the image into 12 sub-bands using DT-CWT. Rotational invariant and scale invariant features are obtained by combining LL and HL sub-bands at each level. Consequently, channel energies and standard deviations are constructed as feature representation of the iris while SVM is used for classification of iris images. Our experiments demonstrate the superiority of the proposed method on CASIA iris databases, over existing methods.

Original languageEnglish
Title of host publicationProceedings - International Conference on Computer Vision and Image Analysis Applications, ICCVIA 2015
PublisherIEEE
ISBN (Electronic)9781479971862
ISBN (Print)9781479971855
DOIs
Publication statusPublished - 10 Dec 2015
EventInternational Conference on Computer Vision and Image Analysis Applications, ICCVIA 2015 - Sousse, Tunisia
Duration: 18 Jan 201520 Jan 2015

Conference

ConferenceInternational Conference on Computer Vision and Image Analysis Applications, ICCVIA 2015
CountryTunisia
CitySousse
Period18/01/1520/01/15

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