A TWO FACTOR AUTHENTICATION SYSTEM BASED ON FACIAL RECOGNITION FOR STUDENT IDENTITY VALIDATION

Abstract

Student identity validation is the most important part of the examination process. When the right student sits for the right examination, the integrity and validity of that examination is upheld. Impersonation in an examination occurs when a student sits in an examination for another student and pretends to be the real student. The student unique identity number is mainly used to determine the validity of a student. However, this method alone does not stop impersonation. This paper therefore develops a biometric system (facial recognition) as a second factor authenticator for student identity validation. The first factor is the unique student identity number and the second factor is the face recognition system. This was achieved by developing a face recognition service model using the python language, OpenCV library and the PyTorch framework. The convolutional Neural Network was adopted and the postgreSQl database was used for the development of the service model. The aim is to mitigate examination impersonation by capturing a live video of every intending student with a HDD camera stationed at the entrance of the examination hall and sending the video captured to a live computer which communicates with the web server to determine whether the student is eligible to sit for that examination. The result of this research effort is a face recognition system which validates students and makes sure that the right student sits for the right examination.