Continuous biometric authentication : can it be more practical?

Alsolami, Eesa, Boyd, Colin, Clark, Andrew, & Khandoker, Asadul Islam (2010) Continuous biometric authentication : can it be more practical? In 12th IEEE International Conference on High Performance Computing and Communications, 1-3 September 2010, Melbourne.

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Continuous biometric authentication schemes (CBAS) are built around the biometrics supplied by user behavioural characteristics and continuously check the identity of the user throughout the session. The current literature for CBAS primarily focuses on the accuracy of the system in order to reduce false alarms. However, these attempts do not consider various issues that might affect practicality in real world applications and continuous authentication scenarios. One of the main issues is that the presented CBAS are based on several samples of training data either of both intruder and valid users or only the valid users' profile. This means that historical profiles for either the legitimate users or possible attackers should be available or collected before prediction time. However, in some cases it is impractical to gain the biometric data of the user in advance (before detection time). Another issue is the variability of the behaviour of the user between the registered profile obtained during enrollment, and the profile from the testing phase. The aim of this paper is to identify the limitations in current CBAS in order to make them more practical for real world applications. Also, the paper discusses a new application for CBAS not requiring any training data either from intruders or from valid users.

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3 citations in Scopus
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ID Code: 38362
Item Type: Conference Paper
Refereed: Yes
Keywords: continuous authentication, continuous biometric authentication system, intruder, impostor
DOI: 10.1109/HPCC.2010.65
ISBN: 9780769542140
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Institutes > Information Security Institute
Copyright Owner: Copyright 2010 IEEE
Deposited On: 11 Nov 2010 05:11
Last Modified: 10 Feb 2015 04:05

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