A survey of human-computer interaction (HCI) & natural habits-based behavioural biometric modalities for user recognition schemes. (July 2023)

• Record Type: Journal Article
• Authors: Gupta, Sandeep; Maple, Carsten; Crispo, Bruno; Raja, Kiran; Yautsiukhin, Artsiom; Martinelli, Fabio
• Is Part Of: Pattern recognition. Volume 139(2023)
• Publication Date: 2023-07
• Subjects: Internet of Things (IoT) -- User recognition -- Behavioural biometrics -- Secutity -- Privacy -- Usability; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2023.109453
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: The article presents a survey of the human-computer interaction and natural habits-based biometrics, namely, touchstroke, swipe, touch-signature, hand-movements, voice, gait, and single footstep that can be acquired from smart devices equipped with motion sensors, touch screens, and microphones or by external IoT sensors or nodes in an unobtrusive manner. The article elicits attributes and features of the aforementioned behavioral biometrics that can be exploited for designing reliable user recognition schemes. We discuss the methodologies, classifiers, datasets, and performance results of recent user recognition schemes that employ these behavioral biometrics modalities. The article presents security, privacy, and usability attributes with regard to the (CIA) properties in human-to-things recognition schemes. The article discusses challenges, limitations, prospects, and opportunities associated with behavioral biometric-based user recognition schemes. The prospects and market trends indicate that behavioral biometrics can instigate innovative ways to implement implicit ( frictionless ), continuous ( active ), or risk-based ( non-static ) recognition schemes for IoT applications. Ultimately, with the availability of smart sensors, advanced machine learning algorithms, and powerful IoT platforms, behavioral biometrics can substitute conventional recognition schemes, thus, reshaping the existing user recognition landscape. Abstract: 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 behavioural 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 human-to-things 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 ( i.e., authentication and identification ) schemes. To address the limitations of conventional recognition schemes, biometrics, 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 behavioural biometrics modalities. This survey analyzes the recent state-of-the-art research of these behavioural 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.