Learning Discriminability-Preserving Histogram Representation from Unordered Features for Multibiometric Feature-Fused-Template Protection

Lim, M-H; Verma, S; Mai, G; Yuen, PC

Learning Discriminability-Preserving Histogram Representation from Unordered Features for Multibiometric Feature-Fused-Template Protection

Lim, M-H Verma, S

Mai, G Yuen, PC

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Publisher:: Elsevier Science Inc.
Publication Type:: Journal Article
Citation:: Pattern Recogn., 2016, 60, pp. 706–719-706–719
Issue Date:: 2016-12

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Field	Value	Language
dc.contributor.author	Lim, M-H
dc.contributor.author	Verma, S https://orcid.org/0000-0003-0458-5985
dc.contributor.author	Mai, G
dc.contributor.author	Yuen, PC
dc.date.accessioned	2022-10-13T22:07:05Z
dc.date.available	2022-10-13T22:07:05Z
dc.date.issued	2016-12
dc.identifier.citation	Pattern Recogn., 2016, 60, pp. 706–719-706–719
dc.identifier.issn	0031-3203
dc.identifier.uri	http://hdl.handle.net/10453/162567
dc.description.abstract	Multi-biometric feature-level fusion exploits feature information from more than one biometric source to improve recognition performance and template security. When ordered and unordered feature sets representing different biometric sources are involved, feature fusion becomes problematic. One way to mitigate this incompatibility problem is to transform the unordered extracted feature sets to ordered feature representation without sacrificing the discrimination power of the original features so that a feature fusion on ordered features can subsequently be applied. Existing unordered-to-ordered feature transformation methods are designed for three-dimensional minutiae point sets and are mostly not adaptable to high-dimensional feature input. This paper proposes a feature transformation scheme to learn a histogram representation from an unordered feature set. Our algorithm estimates the component-wise correspondences among the sample feature sets of each user and then learns a set of bins per user based on the distribution of the mutually-corresponding feature instances. Given the learnt bins, the histogram representation of a sample can be generated by concatenating the normalized frequency of unordered features falling into histogram bins. Experimental results on seven unimodal and three bimodal biometric databases show that our feature transformation scheme is able to preserve the discrimination power of the original features more promisingly than state-of-the-art transformation schemes.
dc.language	en
dc.publisher	Elsevier Science Inc.
dc.relation.ispartof	Pattern Recogn.
dc.relation.isbasedon	10.1016/j.patcog.2016.06.018
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0806 Information Systems, 0906 Electrical and Electronic Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Learning Discriminability-Preserving Histogram Representation from Unordered Features for Multibiometric Feature-Fused-Template Protection
dc.type	Journal Article
utslib.citation.volume	60
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
utslib.for	0906 Electrical and Electronic Engineering
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/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2022-10-13T22:07:03Z
pubs.publication-status	Published
pubs.volume	60

Abstract:

Multi-biometric feature-level fusion exploits feature information from more than one biometric source to improve recognition performance and template security. When ordered and unordered feature sets representing different biometric sources are involved, feature fusion becomes problematic. One way to mitigate this incompatibility problem is to transform the unordered extracted feature sets to ordered feature representation without sacrificing the discrimination power of the original features so that a feature fusion on ordered features can subsequently be applied. Existing unordered-to-ordered feature transformation methods are designed for three-dimensional minutiae point sets and are mostly not adaptable to high-dimensional feature input. This paper proposes a feature transformation scheme to learn a histogram representation from an unordered feature set. Our algorithm estimates the component-wise correspondences among the sample feature sets of each user and then learns a set of bins per user based on the distribution of the mutually-corresponding feature instances. Given the learnt bins, the histogram representation of a sample can be generated by concatenating the normalized frequency of unordered features falling into histogram bins. Experimental results on seven unimodal and three bimodal biometric databases show that our feature transformation scheme is able to preserve the discrimination power of the original features more promisingly than state-of-the-art transformation schemes.

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http://hdl.handle.net/10453/162567