Touchalytics: On the Applicability of Touchscreen Input as a Behavioral Biometric for Continuous Authentication
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TLDR
A classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen is proposed.Abstract:
We investigate whether a classifier can continuously authenticate users based on the way they interact with the touchscreen of a smart phone. We propose a set of 30 behavioral touch features that can be extracted from raw touchscreen logs and demonstrate that different users populate distinct subspaces of this feature space. In a systematic experiment designed to test how this behavioral pattern exhibits consistency over time, we collected touch data from users interacting with a smart phone using basic navigation maneuvers, i.e., up-down and left-right scrolling. We propose a classification framework that learns the touch behavior of a user during an enrollment phase and is able to accept or reject the current user by monitoring interaction with the touch screen. The classifier achieves a median equal error rate of 0% for intrasession authentication, 2%-3% for intersession authentication, and below 4% when the authentication test was carried out one week after the enrollment phase. While our experimental findings disqualify this method as a standalone authentication mechanism for long-term authentication, it could be implemented as a means to extend screen-lock time or as a part of a multimodal biometric authentication system.read more
Citations
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Biometric Authentication Based on Touchscreen Swipe Patterns
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TL;DR: Although authentication EER based on single swipe is around 4%, this was improved by using sequences of 5 swipes (0.2% EER).
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Safeguard: User Reauthentication on Smartphones via Behavioral Biometrics
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TL;DR: The experimental results show that Safeguard can verify a user with 0.03% false acceptance rate (FAR) and 0.05% false rejection rate (FRR) within 0.3 s with 15 to 20 slides by the user.
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Deep Residual Network for Smartwatch-Based User Identification through Complex Hand Movements
TL;DR: In this article , the authors used a one-dimensional residual network with squeeze-and-excitation (SE) configurations called the 1D-ResNet-SE model to investigate hand movements and user identification.
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