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143119 - Continuous multimodal biometric authentication schemes.pdf (3.1 MB)

Continuous multimodal biometric authentication schemes: A systematic review

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journal contribution
posted on 2023-05-20, 21:32 authored by Riseul RyuRiseul Ryu, Soonja YeomSoonja Yeom, Kim, S-H, David HerbertDavid Herbert
Building safeguards against illegitimate access and authentication is a cornerstone for securing systems. Existing user authentication schemes suffer from challenges in detecting impersonation attacks which leave systems vulnerable and susceptible to misuse. A range of research proposals have suggested continuous multimodal biometric authentication (CMBA) systems as a reliable solution. Though contemporary authentication systems have the potential to change their current authentication scheme, there is a lack of critical analysis of current progress in the field to foster and influence practical solutions. This paper provides a systematic survey of existing literature on CMBA systems, followed by analysis to identify and discuss current research and future trends. The study has found that many diverse biometric characteristics are used for multimodal biometric authentication systems. The majority of the studies in the literature reviewed apply supervised learning approaches as a classification technique, and score level fusion is predominantly used as a fusion model. The review has determined however that there is a lack of comparative analysis on CMBA design in terms of combinations of biometric types (behavioural only, physiological only, or both), machine learning algorithms (unsupervised learning and semi-supervised learning), and fusion models. Most of the studies evaluated a CMBA system’s accuracy functionality, such as False Acceptance Rate (FAR), False Rejection Rate (FRR) and Equal Error Rate (EER). However, security, scalability and usability (user acceptance and satisfaction) are generally not addressed thoroughly even though these are key factors for system success in a real deployment. Furthermore, a CMBA system should be implemented and evaluated extensively on real data without restriction to prove that such systems are feasible.


Publication title

IEEE Access








School of Information and Communication Technology


Institute of Electrical and Electronics Engineers

Place of publication

United States

Rights statement

© 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Repository Status

  • Open

Socio-economic Objectives

Artificial intelligence; Cybersecurity

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