Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data

• URL: http://arxiv.org/abs/2008.10779v1
• Date: Tue, 25 Aug 2020 01:55:07 GMT
• Title: Continuous Authentication of Wearable Device Users from Heart Rate, Gait, and Breathing Data
• Authors: William Cheung and Sudip Vhaduri
• Abstract summary: Security of private information is becoming the bedrock of an increasingly digitized society. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. We present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals.
• Score: 1.827510863075184
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The security of private information is becoming the bedrock of an increasingly digitized society. While the users are flooded with passwords and PINs, these gold-standard explicit authentications are becoming less popular and valuable. Recent biometric-based authentication methods, such as facial or finger recognition, are getting popular due to their higher accuracy. However, these hard-biometric-based systems require dedicated devices with powerful sensors and authentication models, which are often limited to most of the market wearables. Still, market wearables are collecting various private information of a user and are becoming an integral part of life: accessing cars, bank accounts, etc. Therefore, time demands a burden-free implicit authentication mechanism for wearables using the less-informative soft-biometric data that are easily obtainable from modern market wearables. In this work, we present a context-dependent soft-biometric-based authentication system for wearables devices using heart rate, gait, and breathing audio signals. From our detailed analysis using the "leave-one-out" validation, we find that a lighter $k$-Nearest Neighbor ($k$-NN) model with $k = 2$ can obtain an average accuracy of $0.93 \pm 0.06$, $F_1$ score $0.93 \pm 0.03$, and {\em false positive rate} (FPR) below $0.08$ at 50\% level of confidence, which shows the promise of this work.

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