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

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

Padma P. Paul, Marina L. Gavrilova
Copyright: © 2013 |Volume: 7 |Issue: 3 |Pages: 15
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781466633896|DOI: 10.4018/ijcini.2013070105
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MLA

Paul, Padma P., and Marina L. Gavrilova. "Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template." IJCINI vol.7, no.3 2013: pp.80-94. http://doi.org/10.4018/ijcini.2013070105

APA

Paul, P. P. & Gavrilova, M. L. (2013). Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 7(3), 80-94. http://doi.org/10.4018/ijcini.2013070105

Chicago

Paul, Padma P., and Marina L. Gavrilova. "Cancelable Fusion of Face and Ear for Secure Multi-Biometric Template," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 7, no.3: 80-94. http://doi.org/10.4018/ijcini.2013070105

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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.

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