A Survey of Human-Computer Interaction (HCI) & Natural Habits-based Behavioral Biometric Modalities for User Recognition Schemes

Abstract

<p>The proliferation of Internet of Things (IoT) systems is having a profound impact across all aspects of life. Recognising and identifying particular users is central to delivering the personalised experience that citizens want to experience, and that organisations wish to deliver. This article presents a survey of human-computer interaction-based (HCI-based) and natural habits-based behavioral biometrics that can be acquired unobtrusively through smart devices or IoT sensors for user recognition purposes. Robust and usable user recognition is also a security requirement for emerging IoT ecosystems to protect them from adversaries. Typically, it can be specified as a fundamental building block for most types of <em>human-to-things</em> accountability principles and access-control methods. However, end-users are facing numerous security and usability challenges in using currently available knowledge- and token-based recognition (<em>i.e., authentication and identification</em>) schemes. To address the limitations of conventional recognition schemes, <em>biometrics</em>, naturally come as a first choice to supporting sophisticated user recognition solutions. We perform a comprehensive review of touch-stroke, swipe, touch signature, hand-movements, voice, gait and footstep behavioral biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioral biometrics with a goal to identify their attributes and features for generating unique identification signatures. Finally, we present security, privacy, and usability evaluations that can strengthen the designing of robust and usable user recognition schemes for IoT applications.</p>