license: mit
🥽 ThermVision-DB: Synthetic Thermal Face Dataset
Author: Muhammad Ali Farooq Affiliation: C3I Group, School of Engineering, University of Galway License: Research-only, non-commercial Modality: Thermal (LWIR) Face Images + Videos Resolution: 512 × 512 Version: 1.0
🧩 Dataset Summary
ThermVision-DB is a synthetic thermal facial dataset generated using thermally tuned FLUX.Dev diffusion-based models and Image-to-Video Translation methods specifically for long-wave IR imaging. The dataset supports research in thermal biometrics, privacy-preserving identity modelling, thermal domain adaptation, and multimodal fusion.
The dataset includes high-quality thermal facial images and controlled thermal video sequences with variations in pose, expression, accessories, and gender representation. All samples are synthetic and contain no personally identifiable information.
🔥 Key Features
Fully synthetic & privacy-preserving
High-resolution thermal faces (512×512)
Balanced gender representation (50 subjects: 25 male, 25 female)
Attribute-rich content: head pose, expressions, accessories, hairstyles
Paired video sequences for each subject
Annotations for gender, identity, and face bounding boxes
Supports training of thermal recognition, domain adaptation, and generative models
📊 Dataset Structure
ThermVision-DB Video Data
| Field | Details |
|---|---|
| Total Subjects / Gender | 50 (25 male, 25 female) — extendable to unlimited synthetic subjects |
| Format & Resolution | MP4, 512×512 |
| Per-Subject Data | 2 video sequences, 604 frames total |
| Total Dataset Size | 100 videos, ~30.2K frames |
| Annotations | Gender, identity labels, face bounding boxes |
| Attributes | Head poses, expressions, hairstyles, accessories, gender parity |
ThermVision-DB 2D Facial Image Data
| Category | Description / Content Type | Resolution | Gender |
|---|---|---|---|
| Beard | Thermal facial images featuring subjects with beards. | 512×512 | Male |
| Facial Accessories | Includes eyewear, masks, hats, or other accessories. | 512×512 | Male / Female |
| Frontal Pose | Frontal-facing thermal face images of various subjects. | 512×512 | Male / Female |
| Mix | Mixed data combining poses, expressions, and variations. | 512×512 | Male / Female |
| Poses | Thermal face images with varying head poses and orientations. | 512×512 | Male / Female |
🧠 Generation Pipeline
Thermal Diffusion Model: Fine-tuned using multi-seed thermal datasets and LoRA modules for identity-preserving synthesis.
Attribute-controlled prompts: Used for generating poses, expressions, accessories, and gender-balanced samples.
Video Generation: Thermal frame interpolation + controlled identity-consistent animation pipeline.
Annotation Pipeline: Automatically extracted face detector bounding boxes + metadata tagging (gender, identity, attributes).
🎯 Intended Use
This dataset is designed for:
Thermal face recognition
Cross-spectral domain adaptation (RGB ↔ thermal)
Privacy-preserving biometrics
Expression & pose analysis in thermal domain
Robust perception for surveillance & low-light environments
Generative modelling & diffusion research
🔐 Ethics & License
All samples are synthetic, contain no real identities, and are generated under a strictly controlled pipeline following FAIR, GDPR, and privacy-preserving design principles.
License: cc-by-nc-4.0
This dataset is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. This license allows for non-commercial use of the dataset, provided proper attribution is given to the authors. Adaptations and modifications are permitted for non-commercial purposes. For full details, please review the CC BY-NC 4.0 license and the license file included in the dataset. As the dataset is gated, you must accept the access conditions on Hugging Face to use it.
✔️ Free for academic and research use ❌ Commercial use prohibited without written permission ❌ Redistribution not allowed
📜 Citation
If you use ThermVision-DB in your research, please cite our dataset and paper
@inproceedings{farooq2025thermvision, title={ThermVision: Exploring FLUX for Synthesizing Hyper-Realistic Thermal Face Data and Animations via Image to Video Translation}, author={Farooq, Muhammad Ali and Shariff, Waseem and Corcoran, Peter}, booktitle={Proceedings of the 33rd ACM International Conference on Multimedia}, pages={10161--10170}, year={2025}