Bibliographic record
Abstract
The conventional methodology for recording student attendance, which heavily relies on manual data transcription, is prone to inefficiencies and errors.Consequently, the development of an automated attendance management system has emerged as a critical need for efficient and accurate maintenance of attendance records.This study presents the design and implementation of an automated attendance management system, exploiting face recognition technology for identifying students within a class setting.A unique dataset was curated, consisting of 3900 facial images, captured in five varying positions and under diverse lighting conditions.In the initial phase of the system's operation, images of students are captured via a mobile camera.Subsequently, the Haar Cascaded classifier is utilized for the detection of faces within these captured images, and the FaceNet network is employed to recognize the detected faces.In the subsequent phase, the system registers attendance by cross-referencing the recognized faces with the primary student record.An attendance sheet copy is then dispatched to the teacher.Upon evaluating the system's effectiveness, it was ascertained that the system successfully identifies students and registers their attendance with an impressive accuracy of 97.5%.It outperforms traditional systems in terms of workload reduction, error avoidance, speed, and accuracy.The proposed system holds potential for widespread deployment in institutes and schools for recording attendance and could be extended for employee attendance recording.By reducing human errors and the time required for attendance registration, and by swiftly generating electronic attendance lists, this system signifies a substantial improvement over conventional systems.
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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 | 0.000 | 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) | 0.007 | 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".