Thermal-Mask – A Dataset for Facial Mask Detection and Breathing Rate Measurement
Bibliographic record
Abstract
This paper demonstrates the usability of thermal video for facial mask detection and the breathing rate measurement. Due to the lack of available thermal masked face images, we developed a dataset based on the SpeakingFaces set, by generating masks for the unmasked thermal images of faces. We utilize the Cascade R-CNN as the thermal facial mask detector, identifying masked and unmasked faces, and whether the mask colour indicates a inhale or exhale state. The latter is used to calculate the breathing rate. The proposed Cascade R-CNN is a multi-stage object detection architecture composed of detectors trained with increasing Intersection-of-Unions thresholds. In our experiments on the Thermal-Mask dataset, the Cascade R-CNN achieves 99.7% in precision, on average, for the masked face detection, and 91.1% for recall. To validate our approach, we also recorded a small set of videos with masked faces to measure the breathing rate. The accuracy result of 91.95% showed a promising advance in identifying possible breath abnormalities using thermal videos, which may be useful in screening subject for COVID-19 symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".