---
pretty_name: Thermal face visibility and nose detection datasets
task_categories:
  - object-detection
tags:
  - yolo
  - yolov5
  - transfer-learning
  - thermal-imaging
  - infrared-thermography
size_categories:
  - 10K<n<100K
license: other
---

# Thermal face visibility and nose detection datasets

This repository contains the YOLO-format annotated thermal frames used to fine-tune the two YOLOv5m models described in the manuscript *Open-standard infrared thermography implementation based on machine learning object identification for studying facial thermal variations at less restrictive settings*.

Direct participant identifiers have been removed from the uploaded filenames. The private correspondence between original and pseudonymized filenames is retained by the data controller and is not part of this repository.

## Dataset origin and annotation

The annotated material was generated by randomly sampling frames from a broader corpus of approximately 1,100 thermal recordings collected across three prior studies conducted with the same camera modules. The recordings lasted between one and five minutes. Sampling was designed to capture variation across participants, recordings, and experimental conditions before manual annotation.

Five laboratory labelers annotated the selected frames with LabelImg using shared written criteria and YOLOv5 bounding-box format. Manual annotation yielded 10,705 frames for the face-visibility task. Of the frames labeled as containing a face, 8,470 additionally received a nose bounding box and, after cropping to the facial region, constituted the nose-detection dataset.

The source recordings were obtained with FLIR Lepton 3.5 modules integrated into PureThermal Mini Pro JST-SR boards. The cameras produced native radiometric streams of 160 × 120 matrices containing 16-bit temperature values in hundredths of a kelvin. The PNG files in this repository are the exported or preprocessed images supplied to the YOLOv5 training workflow.

## Dataset structure

```text
face_visibility/
  data.yaml
  images/{train,val,test}/
  labels/{train,val,test}/
nose_detection/
  data.yaml
  images/{train,val,test}/
  labels/{train,val,test}/
```

Every `.txt` annotation uses normalized YOLO coordinates:

```text
class_id x_center y_center width height
```

## Splits and classes

| Dataset | Classes | Train | Validation | Test | Total |
|---|---|---:|---:|---:|---:|
| Face visibility | `0 = face`, `1 = no_face` | 8,513 | 1,121 | 1,071 | 10,705 |
| Nose detection | `0 = nose` | 6,733 | 889 | 848 | 8,470 |

### Face-visibility class distribution

A direct audit of the published label files produced the following image-level distribution:

| Split | Total images | Only `face` | Only `no_face` | Both classes | Images containing `no_face` | Percentage containing `no_face` |
|---|---:|---:|---:|---:|---:|---:|
| Train | 8,513 | 7,165 | 1,347 | 1 | 1,348 | 15.83% |
| Validation | 1,121 | 944 | 177 | 0 | 177 | 15.79% |
| Test | 1,071 | 904 | 167 | 0 | 167 | 15.59% |
| **Total** | **10,705** | **9,013** | **1,691** | **1** | **1,692** | **15.81%** |

Thus, 1,691 of 10,705 images (15.80%) are annotated exclusively as `no_face`. When the single image containing annotations from both classes is included, 1,692 images (15.81%) contain at least one `no_face` annotation. The difference between these percentages reflects that single mixed-class image.

Each annotated frame occurs in only one subset. The supplied train, validation, and test directories therefore reproduce the frame-level partitions used for model development, with no repeated image between subsets. The detection objective is spatial: identifying usable facial information and locating the nose, rather than predicting participant identity or experimental condition.

## Validation performed before publication

- Every image has a label file with the same stem.
- No orphan label files were found.
- All bounding-box coordinates are within the normalized range `[0, 1]`.
- No unreadable PNG files were detected.
- The original train, validation, and test assignments were preserved.
- Published filenames follow a pseudonymized sequential naming scheme.

## Recommended use

The datasets support reproduction of the reported YOLOv5 transfer-learning workflow and adaptation of pretrained object-detection models to thermal-imaging research. Researchers applying the workflow to a new camera, geometry, population, or recording environment should independently assess performance and fine-tune the models with representative frames when necessary.

## Sensitive-data notice

These files contain low-resolution thermal images of research participants. They must be handled according to informed-consent terms, ethics approval, institutional requirements, and applicable data-use agreements. Do not use the dataset for identity recognition, surveillance, clinical diagnosis, or attempts to re-identify participants.

## Related resources

- [Model checkpoints](https://huggingface.co/LiFLabUC/NoseNet)
- [Participant-independent validation dataset and results](https://huggingface.co/datasets/LiFLabUC/NoseNet-independent-validation)
- [Project source code](https://github.com/liflabuc/very-low-resolution-facial-thermography-yolov5)

## License and access

No reuse license is granted by this card. Access, redistribution, and secondary use remain subject to the terms established by the authors, the responsible institutions, and the applicable participant authorizations.