DEVELOPMENT OF AI-POWERED PROCTORING SYSTEM FOR COMPUTER BASED EXAMINATION

Computer-based tests (CBT) are increasingly used in educational institutions, but ensuring exam integrity remains a challenge due to possibilities of cheating and unauthorized behavior. This final year project report presents the design and integration of the IntelliProctor Vision System, an AI-powered proctoring system tailored for computer-based examinations in resource-constrained environments. Developed as a lightweight web-based application, the system utilizes Flask for backend orchestration, SQLite for local data persistence, and computer vision / AI libraries including OpenCV, InsightFace (for facial recognition and verification), and YOLOv8n (for unauthorized object and person detection), alongside SoundDevice for real-time acoustic monitoring. System Capabilities & Architecture: Continuous Face & Identity Verification: Real-time embedding extraction and comparison to detect impersonation and face mismatches. Behavior & Object Monitoring: Real-time detection of multiple faces, gaze diversion (looking away), and unauthorized physical objects/devices via YOLOv8n. Audio & Screen Activity Tracking: Ambient noise/speech detection and browser-level tab-switching detection. Configurable Fairness Thresholds: Dynamic violation tolerances (e.g., face mismatch: 1, multiple faces: 2, looking away: 4, audio: 2, screen activity: 1) designed to reduce false positives. Audit Logging & Admin Dashboard: Persistent event logging paired with timestamped screenshots and integrity scoring for post-exam review. Key Performance Results: Detection Accuracy: Achieved an overall macro-average F1-score of 0.89 across all detection modules. Edge Performance: Maintained ~18 FPS with 78% CPU utilization on low-end hardware (Intel Celeron, 4GB RAM) with an overall storage footprint of under 1GB. Cloud Prototype Benchmark: Achieved ~32 FPS upon cloud container deployment on Hugging Face Spaces (IbnMuhd/IPS). Submitted to the Department of Computer Engineering, Ahmadu Bello University, Zaria, in partial fulfilment of the requirements for the award of Bachelor of Engineering (B.Eng) Degree in Computer Engineering.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-09-29
DOI
https://doi.org/10.5281/zenodo.23039713
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
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article

DEVELOPMENT OF AI-POWERED PROCTORING SYSTEM FOR COMPUTER BASED EXAMINATION

Ibrahim Yusuf
Zenodo (CERN European Organization for Nuclear Research)
Face recognition and analysis
article

DEVELOPMENT OF AI-POWERED PROCTORING SYSTEM FOR COMPUTER BASED EXAMINATION

Ibrahim Yusuf
article en

Abstract

Computer-based tests (CBT) are increasingly used in educational institutions, but ensuring exam integrity remains a challenge due to possibilities of cheating and unauthorized behavior. This final year project report presents the design and integration of the IntelliProctor Vision System, an AI-powered proctoring system tailored for computer-based examinations in resource-constrained environments. Developed as a lightweight web-based application, the system utilizes Flask for backend orchestration, SQLite for local data persistence, and computer vision / AI libraries including OpenCV, InsightFace (for facial recognition and verification), and YOLOv8n (for unauthorized object and person detection), alongside SoundDevice for real-time acoustic monitoring. System Capabilities & Architecture: Continuous Face & Identity Verification: Real-time embedding extraction and comparison to detect impersonation and face mismatches. Behavior & Object Monitoring: Real-time detection of multiple faces, gaze diversion (looking away), and unauthorized physical objects/devices via YOLOv8n. Audio & Screen Activity Tracking: Ambient noise/speech detection and browser-level tab-switching detection. Configurable Fairness Thresholds: Dynamic violation tolerances (e.g., face mismatch: 1, multiple faces: 2, looking away: 4, audio: 2, screen activity: 1) designed to reduce false positives. Audit Logging & Admin Dashboard: Persistent event logging paired with timestamped screenshots and integrity scoring for post-exam review. Key Performance Results: Detection Accuracy: Achieved an overall macro-average F1-score of 0.89 across all detection modules. Edge Performance: Maintained ~18 FPS with 78% CPU utilization on low-end hardware (Intel Celeron, 4GB RAM) with an overall storage footprint of under 1GB. Cloud Prototype Benchmark: Achieved ~32 FPS upon cloud container deployment on Hugging Face Spaces (IbnMuhd/IPS). Submitted to the Department of Computer Engineering, Ahmadu Bello University, Zaria, in partial fulfilment of the requirements for the award of Bachelor of Engineering (B.Eng) Degree in Computer Engineering.

Zenodo (CERN European Organization for Nuclear Research)
Ahmadu Bello University (NG)
Openalex Percentile: Top 14%
Face recognition and analysis
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DEVELOPMENT OF AI-POWERED PROCTORING SYSTEM FOR COMPUTER BASED EXAMINATION — Ibrahim Yusuf · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS