LEARNER AUTHENTICATION VIA BIOMETRIC-BASED MULTI-FACTOR
Main Article Content
Abstract
Online learning systems, particularly systems for remote student assessment, face a challenge in student authentication. Currently, the most common authentication method for these systems is the username-password approach, which is easy to use but requires users to create strong and complex passwords that are often difficult to remember. Some online learning systems have adopted biometric authentication methods, which enable learners to log in without a password. However, relying on a single biometric factor presents several issues, particularly the stability of biometric information. This article proposes a multi-factor authentication solution that combines facial and voice biometrics. The proposed approach was tested on the VoxCeleb1 dataset and achieved exceptional results, with an accuracy of 99.6%. By comparison, facial recognition alone yielded 95.62% accuracy, and voice recognition alone achieved 98.66%.
Keywords
authentication, biometrics, biometric multi-factor, biometric single-factor, learners authentication, online learning system
Article Details
References
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