A framework for liveness detection for direct attacks in the visible spectrum for multimodal ocular biometrics

Das, A; Pal, U; Ferrer, MA; Blumenstein, M

A framework for liveness detection for direct attacks in the visible spectrum for multimodal ocular biometrics

Das, A Pal, U Ferrer, MA Blumenstein, M

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Publication Type:: Journal Article
Citation:: Pattern Recognition Letters, 2016, 82 pp. 232 - 241
Issue Date:: 2016-10-15

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Field	Value	Language
dc.contributor.author	Das, A	en_US
dc.contributor.author	Pal, U	en_US
dc.contributor.author	Ferrer, MA	en_US
dc.contributor.author	Blumenstein, M https://orcid.org/0000-0002-9908-3744	en_US
dc.date.issued	2016-10-15	en_US
dc.identifier.citation	Pattern Recognition Letters, 2016, 82 pp. 232 - 241	en_US
dc.identifier.issn	0167-8655	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43679
dc.description.abstract	© 2015 Elsevier B.V. In this research a new framework for software-based liveness detection for direct attacks in multimodal ocular biometrics across the visible spectrum is proposed. The framework aims to develop a more realistic method for liveness detection compared to previous frameworks proposed in the literature. To fulfil the above highlighted aims in this framework, intra-class level (i.e. user level) liveness detection is introduced. To detect liveness, a new set of image quality-based features is proposed for multimodal ocular biometrics in the visible spectrum. A variety of transformed domain (focus related) aspect and contrast-related quality features are employed to design the framework. Furthermore a new database is developed for liveness detection of multimodal ocular biometrics, which has the prominent presence of multimodal ocular traits (both sclera and iris). Moreover this database is comprised of a larger variety of fake images; those were prepared by employing versatile forging techniques which can be exhibited by imposters. Therefore the proposed schema has dealt with versatile categories of spoofing methods, which were not considered previously in the literature. The database contains a set of 500 fake and 500 genuine eye images acquired from 50 different eyes. An appreciable liveness detection result is achieved in the experiments. Furthermore, the experimental results conclude that this new framework is more efficient and competitive when compared to previous liveness detection schemes.	en_US
dc.relation.ispartof	Pattern Recognition Letters	en_US
dc.relation.isbasedon	10.1016/j.patrec.2015.11.016	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	A framework for liveness detection for direct attacks in the visible spectrum for multimodal ocular biometrics	en_US
dc.type	Journal Article
utslib.citation.volume	82	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1702 Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
pubs.organisational-group	/University of Technology Sydney/Strength - QSI - Centre for Quantum Software and Information
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	82	en_US

Abstract:

© 2015 Elsevier B.V. In this research a new framework for software-based liveness detection for direct attacks in multimodal ocular biometrics across the visible spectrum is proposed. The framework aims to develop a more realistic method for liveness detection compared to previous frameworks proposed in the literature. To fulfil the above highlighted aims in this framework, intra-class level (i.e. user level) liveness detection is introduced. To detect liveness, a new set of image quality-based features is proposed for multimodal ocular biometrics in the visible spectrum. A variety of transformed domain (focus related) aspect and contrast-related quality features are employed to design the framework. Furthermore a new database is developed for liveness detection of multimodal ocular biometrics, which has the prominent presence of multimodal ocular traits (both sclera and iris). Moreover this database is comprised of a larger variety of fake images; those were prepared by employing versatile forging techniques which can be exhibited by imposters. Therefore the proposed schema has dealt with versatile categories of spoofing methods, which were not considered previously in the literature. The database contains a set of 500 fake and 500 genuine eye images acquired from 50 different eyes. An appreciable liveness detection result is achieved in the experiments. Furthermore, the experimental results conclude that this new framework is more efficient and competitive when compared to previous liveness detection schemes.

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http://hdl.handle.net/10453/43679