Emerging bimodal biometrics authentication for non-venue-based assessments in open distance e-learning (OdeL) environments

Authentication plays a significant role in security during the non-venue-based examination, as it verifies the identity of online students. Existing authentication techniques have not yet provided an optimal cheating-free, non-venue-based assessment. This research proposes an emerging biometrics technology model of facial recognition and keystroke dynamics (FRAKD) to minimise the problems of malpractice during examinations significantly. Detailed experiments conducted on the Moodle learning management system (LMS), using a client-server platform of 100 diverse students who were captured as a positive data set, produced an average recognition rate (RR) of 85%, while for 20 imposter students, an average rejection rate of 90% was realised. When benchmarked with related solutions, this result shows that using the FRAKD biometrics model in non-venue-based assessment demonstrates reliable security and minimises impersonation more than state-of-the-art practices such as portfolio-based distance assessment without supervision. This improved solution will obviously have great value for ODeL institutions and examination bodies.