A Low Cost Prototype of A Real-Time Face-Mask Detection and Enforcement System Using CNN and Arduino for Developing Countries
Bibliographic record
Abstract
Thanks to advancements in Artificial Intelligence (AI), particularly in Machine Learning (ML), public health safety can be maintained more efficiently. Rather than risking the health of security personnel who must ensure compliance with face-mask mandates, ML techniques can automate this process with high efficiency. This paper introduces a prototype for a comprehensive face-mask detection system that not only detects face-masks but also controls access to secured buildings and dispenses masks to those without one. Initially, a Convolutional Neural Network (CNN)-based system with the help of a webcam, identifies whether an individual is wearing a mask. Depending on the detection outcome, a hardware door lock controlled by an Arduino chip either grants or restricts entry. If an individual is not wearing a mask, the system activates a mask dispenser. Once the individual puts on a mask, the system verifies its presence and allows entry. This integrated approach ensures that only masked individuals can access the building, enhancing public health protection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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 teacher head, 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".