EXAMINATION MALPRACTICE PANACEA: A DEEP LEARNING APPROACH USING MOBILENETV2
Abstract
Examination malpractice is like a cancer that has eaten deep into the mainstream of Education in secondary and tertiary institutions in developing countries such as Nigeria. If not tackled holistically tends to destroy the aim behind achieving it. In this work, the use of deep learning in facial recognition using the MobileNetV2 model was proposed as a game changer towards the curbing of examination malpractices (impersonation) in the institution of learning. The result obtained after the model training indicates strong performance with an accuracy of 0.944 which is a little bit shy of the perfect accuracy of 1.00. Technically, this showed that when implemented digitally in learning institutions, it will be able to facially recognize students who collude with one another for impersonation, thereby significantly reducing examination malpractices.
KEYWORDS: deep learning, examination malpractice, facial recognition, mobilenetv2
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