Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template

Padma P. Paul and Marina L. Gavrilova
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Padma P. Paul: Department of Computer Science, University of Calgary, Calgary, Canada
Marina L. Gavrilova: Department of Computer Science, University of Calgary, Calgary, Canada

International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 2013, vol. 7, issue 3, 80-94

Abstract: Biometric fusion to achieve multimodality has emerged as a highly successful new approach to combat problems of unimodal biometric system such as intraclass variability, interclass similarity, data quality, non-universality, and sensitivity to noise. The authors have proposed new type of biometric fusion called cancelable fusion. The idea behind the cancelable biometric or cancelability is to transform a biometric data or feature into a new one so that the stored biometric template can be easily changed in a biometric security system. Cancelable fusion does the fusion of multiple biometric trait in addition it preserve the properties of cancelability. In this paper, the authors present a novel architecture for template generation within the context of the cancelable multibiometric fusion. The authors develop a novel cancelable biometric template generation algorithm using cancelable fusion, random projection and transformation-based feature extraction and selection. The authors further validate the performance of the proposed algorithm on a virtual multimodal face and ear database.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jcini0:v:7:y:2013:i:3:p:80-94

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