Face Recognition - Based Attendance Management System
Bibliographic record
Abstract
Face recognition technology has emerged as a leading solution for secure, contactless, and fully automated attendance tracking in academic and organizational environments. This research presents a hybrid face recognition–based attendance management system combining Local Binary Pattern Histogram (LBPH) and Convolutional Neural Network (CNN) models to achieve high recognition accuracy, robustness, and real-time performance. The system includes webcam-based face acquisition, preprocessing, feature extraction, classification, and secure attendance logging. Experiments conducted using institutional datasets and the Labeled Faces in the Wild (LFW) dataset show that the hybrid model achieves up to 96% accuracy, outperforming standalone LBPH (88%) and CNN (92%) models. The system handles variations in lighting, pose, and occlusion effectively. This work demonstrates that the proposed solution is more hygienic, cost-effective, and fraud-resistant than traditional attendance methods, contributing significantly to modern smart campus ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.015 |
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; both teacher heads agree on what is shown here.
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".