A k-NN Classification based VR User Verification using Eye Movement and Ocular Biomechanics

Iskander, J; Abobakr, A; Attia, M; Saleh, K; Nahavandi, D; Hossny, M; Nahavandi, S

A k-NN Classification based VR User Verification using Eye Movement and Ocular Biomechanics

Iskander, J Abobakr, A Attia, M Saleh, K Nahavandi, D Hossny, M Nahavandi, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 1844-1848
Issue Date:: 2019

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Field	Value	Language
dc.contributor.author	Iskander, J
dc.contributor.author	Abobakr, A
dc.contributor.author	Attia, M
dc.contributor.author	Saleh, K
dc.contributor.author	Nahavandi, D
dc.contributor.author	Hossny, M
dc.contributor.author	Nahavandi, S
dc.date	2019-10-06
dc.date.accessioned	2020-06-02T21:06:58Z
dc.date.available	2020-06-02T21:06:58Z
dc.date.issued	2019
dc.identifier.citation	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), 2019, 2019-October, pp. 1844-1848
dc.identifier.isbn	978-1-7281-4569-3
dc.identifier.issn	2577-1655
dc.identifier.uri	http://hdl.handle.net/10453/141057
dc.description.abstract	VR user identification is of utmost importance especially with the increased applications of VR that will include e-payment among other applications that requires a high level of security. Biometric identification through eye movement, has been used previously due to the intrinsic characteristics of eye movement that characterises a person uniquely. In this paper, we propose using eye movement along with extraocular muscle activations in VR user verification. The muscle activations are calculated using an ocular biomechanical model. The k-NN classification results showed approximately 90% accuracy when using a feature set with eye movement parameters (3 joint angles), muscle activations for all 6 muscles along with the VR object position in 3D. The classifier is a biometric VR user verification tool that provides an easy and non-intrusive methods that can be easily integrated in different VR applications that require user verification.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)
dc.relation.ispartof	IEEE International Conference on Systems, Man and Cybernetics
dc.relation.isbasedon	10.1109/SMC.2019.8914577
dc.rights	info:eu-repo/semantics/closedAccess
dc.rights	© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	A k-NN Classification based VR User Verification using Eye Movement and Ocular Biomechanics
dc.type	Conference Proceeding
utslib.citation.volume	2019-October
utslib.location.activity	Bari, Italy
utslib.for	0903 Biomedical Engineering
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-06-02T21:06:53Z
pubs.finish-date	2019-10-09
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-10-06
pubs.volume	2019-October
utslib.start-page	1844
dc.location	Piscataway, USA

Abstract:

VR user identification is of utmost importance especially with the increased applications of VR that will include e-payment among other applications that requires a high level of security. Biometric identification through eye movement, has been used previously due to the intrinsic characteristics of eye movement that characterises a person uniquely. In this paper, we propose using eye movement along with extraocular muscle activations in VR user verification. The muscle activations are calculated using an ocular biomechanical model. The k-NN classification results showed approximately 90% accuracy when using a feature set with eye movement parameters (3 joint angles), muscle activations for all 6 muscles along with the VR object position in 3D. The classifier is a biometric VR user verification tool that provides an easy and non-intrusive methods that can be easily integrated in different VR applications that require user verification.

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http://hdl.handle.net/10453/141057