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