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Continuous multimodal biometric authentication schemes: A systematic review


Ryu, R and Yeom, S and Kim, S-H and Herbert, D, Continuous multimodal biometric authentication schemes: A systematic review, IEEE Access, 9 pp. 34541-34557. ISSN 2169-3536 (2021) [Refereed Article]

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Copyright 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.

DOI: doi:10.1109/ACCESS.2021.3061589


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 systemís 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.

Item Details

Item Type:Refereed Article
Keywords:biometrics access control, continuous authentication, machine learning algorithms, multimodal
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Machine learning not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Ryu, R (Miss Riseul Ryu)
UTAS Author:Yeom, S (Dr Soonja Yeom)
UTAS Author:Herbert, D (Dr David Herbert)
ID Code:143119
Year Published:2021
Web of Science® Times Cited:9
Deposited By:Information and Communication Technology
Deposited On:2021-02-28
Last Modified:2021-09-09
Downloads:18 View Download Statistics

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