Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)

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Application of Biometric Technology in Academic Examinations

Authors
Qiman Huang1, *
1School of Mechanical Engineering, Tianjin University of science & technology, Tianjin, 300000, China
*Corresponding author. Email: hqm2081512549@outlook.com
Corresponding Author
Qiman Huang
Available Online 24 April 2026.
DOI
10.2991/978-94-6239-648-7_11How to use a DOI?
Keywords
Facial Recognition; Behavior Analysis; Gaze Detection; Examination
Abstract

Educational examinations are critical for national talent selection. In recent years, the number of examinees has surged due to growing demands for academic advancement, professional title evaluations, and occupational qualifications. This expansion brings new management challenges, as conventional cheating—such as note-smuggling, technical violations, forged IDs, and proxy test-taking—persists despite bans. Manual verification worsens these issues, being cumbersome, time-consuming, and prone to subjective bias. Thus, curbing cheating via effective means is urgent, and biometric technology (integrating facial recognition, behavior analysis, and gaze detection) offers a solution for an anti-cheating system. The biometric framework in this study has two key functions. First, facial recognition’s ultra-high-precision matching verifies examinees’ identities against ID data, eliminating identity fraud (e.g., forged documents, proxy tests) at the source. Second, integrated behavior recognition and gaze tracking capture body movements and eye trajectories, enabling real-time pre-alerts for in-exam cheating (e.g., whispering, furtive glances) and overcoming traditional proctoring’s latency. This research provides a rigorous theoretical basis for shifting exam administration from human-dominated to intelligent collaboration, plus feasibility analysis for technical deployment. Widespread use of this system will boost oversight precision and efficacy, safeguard exam fairness, strengthen the talent-selection mechanism’s credibility, and build a more standardized, trustworthy exam ecosystem.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
Series
Advances in Computer Science Research
Publication Date
24 April 2026
ISBN
978-94-6239-648-7
ISSN
2352-538X
DOI
10.2991/978-94-6239-648-7_11How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Qiman Huang
PY  - 2026
DA  - 2026/04/24
TI  - Application of Biometric Technology in Academic Examinations
BT  - Proceedings of the International Workshop on Advances in Deep Learning for Image Analysis and Computer Vision (IWADIC 2025)
PB  - Atlantis Press
SP  - 90
EP  - 96
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6239-648-7_11
DO  - 10.2991/978-94-6239-648-7_11
ID  - Huang2026
ER  -