Continuous Multimodal Biometric Authentication Schemes: A Systematic Review

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