Enhanced Biometric Template Protection Schemes for Securing Face Recognition in IoT Environment

Sardar, A; Umer, S; Rout, RK; Sahoo, KS; Gandomi, AH

Enhanced Biometric Template Protection Schemes for Securing Face Recognition in IoT Environment

Sardar, A Umer, S Rout, RK Sahoo, KS Gandomi, AH

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, 11, (13), pp. 23196-23206
Issue Date:: 2024-07-01

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Field	Value	Language
dc.contributor.author	Sardar, A
dc.contributor.author	Umer, S
dc.contributor.author	Rout, RK
dc.contributor.author	Sahoo, KS
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-08-07T05:16:57Z
dc.date.available	2024-08-07T05:16:57Z
dc.date.issued	2024-07-01
dc.identifier.citation	IEEE Internet of Things Journal, 2024, 11, (13), pp. 23196-23206
dc.identifier.issn	2372-2541
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/180292
dc.description.abstract	With the increasing use of biometrics in Internet of Things (IoT)-based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: 1) face recognition and 2) face biometric template protection. The face recognition includes face image preprocessing by the tree structure part model (TSPM), feature extraction by ensemble patch statistics (EPS) technique, and user classification by multiclass linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR)-based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-D cancelable feature vectors computed from the 500-D original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2024.3374229
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Enhanced Biometric Template Protection Schemes for Securing Face Recognition in IoT Environment
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-08-07T05:16:53Z
pubs.issue	13
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	13

Abstract:

With the increasing use of biometrics in Internet of Things (IoT)-based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: 1) face recognition and 2) face biometric template protection. The face recognition includes face image preprocessing by the tree structure part model (TSPM), feature extraction by ensemble patch statistics (EPS) technique, and user classification by multiclass linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR)-based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-D cancelable feature vectors computed from the 500-D original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection.

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http://hdl.handle.net/10453/180292