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Multi-modal Behavioural Biometric Authentication for Mobile Devices

Abstract : The potential advantages of behavioural biometrics are that they can be utilised in a transparent (non-intrusive) and continuous authentication system. However, individual biometric techniques are not suited to all users and scenarios. One way to increase the reliability of transparent and continuous authentication systems is create a multi-modal behavioural biometric authentication system. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that behaviour profiling, keystroke dynamics and linguistic profiling can be used to discriminate users with overall error rates 20%, 20% and 22% respectively. To study the feasibility of multi-modal behaviour biometric authentication system, matching-level fusion methods were applied. Two fusion methods were utilised: simple sum and weight average. The results showed clearly that matching-level fusion can improve the classification performance with an overall EER 8%.
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Hataichanok Saevanee, Nathan Clarke, Steven Furnell. Multi-modal Behavioural Biometric Authentication for Mobile Devices. 27th Information Security and Privacy Conference (SEC), Jun 2012, Heraklion, Crete, Greece. pp.465-474, ⟨10.1007/978-3-642-30436-1_38⟩. ⟨hal-01518234⟩



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