Design and Development of Automated Student Attendance Framework in Fusion of CNN, HAAR, and ResNet
Bibliographic record
Abstract
Traditional university attendance systems, whether manual or biometric, are generally inefficient, prone to fraud, and have large operational costs.This research study solves these issues by providing an automated attendance tracking system based on facial recognition, which eliminates the need for human supervision while increasing precision.To recognize and extract facial features, the system uses a fused deep learning model that combines ResNet-based Convolutional Neural Networks (CNN), pretrained U-NET, and HAAR cascade techniques.The model was trained using a dataset of 1,120 facial photos per participant, which included nine and eleven-layer CNN architectures with a variety of activation functions such as ReLU, SoftMax, and Tanh.The system, built with Python and OpenCV, extracts 68 facial landmarks per face and functions under a variety of lighting and environmental circumstances.The suggested algorithm achieves 97.81% accuracy in recognition while significantly lowering false positives by 3.03%, 2.03%, and 1.48% when compared to ResNet18, ResNet34, and ResNet50.Furthermore, the computational efficiency of the TensorFlow and CoreML frameworks was evaluated in order to determine their suitability for implementation on embedded devices.The findings show that the approach is effective in real-time attendance settings and has the potential to improve existing institutional tracking 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".