Identity Verification and Fraud Detection During Online Exams with a Privacy Compliant Biometric System

M. Haytom, C. Rosenberger, C. Charrier, C. Zhu, C. Regnier

Abstract

Distant learning is an alternative solution to education when the learner is far from the school or cannot attend courses for professional or medical reasons. The main objective of this work is to design a smart application of remote exams, using a multibiometric system combining face with deep learning and keystroke dynamics to verify the identity of the learner. Privacy protection is consider in this work as an important issue because many personal data are processed in the proposed solution. We consider in this paper experiments under real-life conditions to identify abnormal behaviours with confidence indicators. We show the system ability to make the correct decision while preserving learner’s privacy.

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