ENHANCING AUTHENTICATION EFFICIENCY IN COMPUTER-BASED EXAMINATIONS THROUGH ADVANCED FACE RECOGNITION SYSTEMS

Abstract

Compared to more conventional techniques such as fingerprint recognition, the use of biometric authentication systems in computer-based examinations offers several advantages. To overcome the disadvantages of fingerprint biometrics, such as high administrative costs, long authentication times, and accuracy issues, this paper proposes an innovative solution that leverages state-of-the- art facial recognition technology. Although biometric fingerprint systems are reliable to a certain extent, they present significant challenges in the context of computer-assisted examinations. The authentication process can be time-consuming and can result in delays and logistical problems. These systems can also be prone to errors, including false positives and false negatives, compromising the integrity of the investigation process. These limitations require research into more efficient and accurate biometric solutions. The developed system uses FaceNet and Multi- Task Cascaded Convolutional Neural Network (MTCNN) algorithms in achieving superior accuracy, efficiency, as well as security in verifying candidates' identities during exams. FaceNet excels at facial recognition by mapping faces in a compact Euclidean space, ensuring high accuracy even with slight variations in facial expressions, angles or lighting conditions. MTCNN increases the robustness of the system through precise face detection and alignment, which are critical for reliable performance. The results show that the facial recognition system outperforms traditional fingerprint-based methods. Accuracy is significantly improved, reducing misidentifications, while streamlining the authentication process, minimizing delays, and improving overall efficiency. The robustness of the system ensures consistent performance despite environmental fluctuations.