Computer Vision Based Automated Attendance System Using Face Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Face recognition technology has gained substantial attention owing to its diverse applications. One of the applications include face recognition-based attendance system which stands out the most among all the existing attendance systems because of its heightened security and time-saving capabilities. Face recognition system is the process of recognizing an individual based only on their facial traits. This paper proposes a real-time face recognition attendance system which validates the real-time monitoring of the process. OpenCV has been used to create a Haar cascade classifier, which is used to recognize faces. The face recognition algorithm Local Binary Pattern Histogram (LBPH) has been chosen in this system due to its robustness and better applicability in the real world. This proposed method is capable of identifying faces of individuals effectively from various angles. The results prove the validation of the work through the monitoring of students' attendance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it