Multimodal biometric authentication of identical twin users in e-learning platforms: Security architecture

This article explores the challenges of the identical twins’ authentication in online learning environments, where the identity substitution can compromise the fairness of assessments. The paper proposes a multimodal and continuous authentication system that combines face (static biometric) together with voice and keystroke dynamics (behavioural biometrics), adapted to the subtle differences between twins. The research highlights the effectiveness of the proposed system in preventing frauds and aims to the design and development of efficient security solutions for e-Learning platforms, considering the biometric similarities of twin users.