Scientific publications
* (Corresponding author)
Other affiliations: University of Johannesburg
Stéphane Gael R. Ekodeck
Christophe Armel Nteme
Daramy Vandi Von Kallon
Jocelyn Edinio Zacko Gbadoubissa
Systems and Soft Computing
Online services, particularly e-learning platforms, face significant challenges in authenticating users due to the absence of physical identification. This vulnerability can lead to security breaches that compromise the credibility of assessments. A robust authentication mechanism is crucial during the evaluation phase to ensure integrity and fairness. A two-phase, multi-factor authentication framework is presented to strengthen security in e-learning environments. The first phase involves user authentication through credential submission and a OTP (One-Time Password) sent by SMS or email, establishing a 2FA (Two-Factor Authentication) process. The second phase employs real-time facial recognition during online examinations, utilizing a feature-based face detection technique with the Haar Cascade classifier and webcam images captured during registration. The experimental results show an authentication accuracy of 80% in well lit conditions and 62% in low light environments, indicating a substantial improvement in security over existing methods. This approach provides a minimally intrusive but effective means of improving the reliability of online assessments. • This work propose a two-phase 3-Factor authentication process to improve access and increase the credibility of online assessment. • It is a real-time facial recognition. • The proposed solution presents less cumbersome for the user and more efficient for online authentication.
