A deep learning-driven fingerprint verification model for enhancing exam integrity in Moroccan higher education

ABSTRACT

In Moroccan higher education, the integrity of examinations is paramount, yet it faces the persistent challenge of identity impersonation. This form of academic dishonesty not only undermines the credibility of educational institutions but also contributes to the graduation of incompetent students. In an era where artificial intelligence is revolutionizing various sectors, its application to upholding academic integrity is both timely and essential. This paper proposes an advanced deep learning-driven fingerprint verification model specifically designed to combat impersonation in university examinations. Unlike traditional methods, our model leverages the power of Siamese neural networks (SNN), renowned for their effectiveness in learning distinct features and similarities. The model, trained, validated, and tested using the SOCOFing dataset, demonstrated high accuracy and effectiveness in fingerprint identification, crucial for verifying identities in educational exam settings. It achieved an accuracy of 99.29%, and an F1 score of 99.27%, surpassing other systems and significantly contributing to examination integrity in Moroccan higher education.

KEYWORDS:

• Biometric identification
• fingerprint authentication
• hall examination
• impersonation
• Siamese neural networks (SNN)

Disclosure statement

No potential conflict of interest was reported by the author(s).