ACE: Automated Exam Control and E-Proctoring System Using Deep Face Recognition
Bibliographic record
Abstract
With the advent of technology, online exams have become the norm in various government exams such as JEE, NEET, and CET. However., the verification of students during these exams still remains a manual process, leading to potential cases of impersonation and fraud. To address this issue, we propose a system that incorporates automated registration, verification and live proctoring using advanced face recognition and speech technologies. In this paper, we present the integration of existing technologies for an exam management system. The objective of the system is to simplify the registration process, ensure the authenticity of students, and provide live proctoring during exams. Our scope includes designing and implementing the system, evaluating its performance, and suggesting future improvements. The system allows for capturing and storing student details and pictures, cross-verifying pictures during the exam, and live proctoring to monitor the exam environment. Additionally, the system provides an easy-to-use attendance sheet that can be exported to PDF. Our proposed system uses VGG model for face recognition which is a deep learning model that adopts a Convolutional Neural Network (CNN) architecture to acquire knowledge about features from facial images. By extracting features from the images, the model can compare and match them with the existing database pictures, thereby ensuring accurate verification.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".