A classifications framework for continuous biometric authentication (2018–2024)

Abstract

The increase in personal devices, the amount of private and sensitive information these devices store/process, and the importance of this information have introduced vital security requirements for user authentication to facilitate data access and collection. Continuous Biometric Authentication is a set of techniques developed to monitor a person's biometrics continuously and ensures transparent/implicit authentication. These protocols could mitigate the security and usability limitations of one-time authentication mechanisms in personal computers and mobile devices. As a result, the popularity of continuous authentication technologies in research has drastically increased, leading to a multitude of different biometric data sampling techniques. These techniques include physiological versus behavioural systems or unimodal versus multimodal authenticators. This paper compares the various data sampling approaches by examining 80 recent state-of-the-art papers and outlining their respective advantages and disadvantages. Firstly, the paper introduces the proposed Continuous Biometric framework, including a diagram detailing its specifics and the rationale for focusing on biometric data sampling. It then explains the system architecture and how our framework integrates with it. Following which, the framework compares the surveyed papers across several popular authentication metrics. Lastly, the paper discusses the challenges that need to be addressed for the widespread adoption of this technology in everyday commercial use.