TITLE:
Formation of an Original Database and Development of Innovative Deep Learning Algorithms for Detecting Face Impersonation in Online Exams
AUTHORS:
Konan Yao, Tiémoman Kone, Venance Saho Zoh
KEYWORDS:
Online Exams, Face Recognition, Convolutional Neural Networks, Data, Bias
JOURNAL NAME:
Open Journal of Applied Sciences,
Vol.13 No.12,
December
6,
2023
ABSTRACT: The
issue related to the risk of identity impersonation, where one person can be
replaced by another in online exam surveillance systems, poses challenges. This
study focuses on the effectiveness of detecting attempts of identity
impersonation through face substitution during online exams, with the aim of
ensuring the integrity of assessments. The goal is to develop facial
recognition algorithms capable of precisely detecting these impersonations,
training them on a tailored database rather than biased generic data. An
original database of student faces has been created. An algorithm leveraging
advanced deep learning techniques such as depthwise separable convolution
has been developed and evaluated on this database. We achieved very high levels
of precision, reaching an accuracy rate of 98% in face detection and
recognition.