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Iris Feature Extraction Using Principally Rotated Complex Wavelet Filters (PR-CWF)

Lookup NU author(s): Charles Ukpai, Professor Satnam Dlay, Dr Wai Lok Woo

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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 theta of the PD. Then, complex wavelet filters CWFs are constructed and rotated in the direction theta of the PD and also in the opposite direction - theta for decomposition of the image into 12 subbands 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.


Publication metadata

Author(s): Ukpai CO, Dlay SS, Woo WL

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Computer Vision and Image Analysis Applications (ICCVIA)

Year of Conference: 2015

Print publication date: 01/01/2015

Acceptance date: 01/01/1900

Publisher: IEEE

URL: http://dx.doi.org/10.1109/ICCVIA.2015.7351904

DOI: 10.1109/ICCVIA.2015.7351904

Library holdings: Search Newcastle University Library for this item

ISBN: 9781479971862


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