FACETICKET: EXAM HALL AUTHENTICATION SYSTEM USING FACE BIOMETRICS

INFORMATION SCIENCE AND ENGINEERING

Authentication has always been the biggest obstacle throughout exams. Academic stakeholders must look for alternative ways to authenticate students for examinations because the manual paper-based clearance process is fundamentally flawed. The method of authenticating a student for an examination has obvious problems, such as the presentation of a fake clearance card, impersonation, and so on. The unethical manner associated with the examination is a grim issue. Identity cards and hall passes are typically utilised in the examination procedure to detect fraudulent activity. The current examination method primarily uses biometric systems and document image analysis tools to identify, recognise, and classify candidates. While the suggested methodology focuses on the picture or video for analysis, fraud is typically found through document image analysis. In this project, a deep learning model is created to automatically recognise faces and identify candidates who are impersonating them in an exam system. Convolutional neural networks, which are utilised in automated face detection and identification systems, can also be employed in exam systems for hall numbering, seating assignments, identity verification, and attendance tracking. The enrollment and authentication processes make up the two stages of the suggested deep learning model. We used a unique dataset that we had gathered ourselves in order to validate the suggested methodology.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.