A new extension of kernel principal component analysis for finger vein authentication

Sepehr Damavandinejadmonfared, Vijay Varadharajan

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

Abstract

In this paper, we introduce a new method of data transformation for finger vein recognition system. Our proposed method uses kernel mapping functions to map the data before performing Principal Component Analysis. Kernel Principal Component Analysis (KPCA) is a well-known extension of PCA which is suitable for finding nonlinear patterns as it maps the data nonlinearly. In this work we develop an extension of KPCA which is both faster and more appropriate than KPCA for finger vein recognition system. The proposed method is called Feature Dependent Kernel Principal Component Analysis (FDKPCA). In FDKPCA the data is mapped differently from KPCA resulting in lower-dimension feature space where more important and valuable features are selected and extracted. Furthermore, extensive experiments reveal the significance of the proposed method for finger vein recognition systems.

Original languageEnglish
Title of host publicationProceedings of the 38th Australasian Computer Science Conference, ACSC 2015
EditorsDavid Parry
Place of PublicationSydney
PublisherAustralian Computer Society
Pages59-63
Number of pages5
Volume159
ISBN (Print)9781921770418
Publication statusPublished - 2015
EventProceedings of the 38th Australasian Computer Science Conference, ACSC 2015 - Sydney, Australia
Duration: 27 Jan 201530 Jan 2015

Publication series

NameConferences in Research and Practice in Information Technology - CRPIT
PublisherAustralian Computer Society
ISSN (Print)1445-1336

Other

OtherProceedings of the 38th Australasian Computer Science Conference, ACSC 2015
CountryAustralia
CitySydney
Period27/01/1530/01/15

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