thermal-face-alignment 1.0.0a2

implementation and weights for facial landmarks in thermal images trained with the dataset described in 'T-FAKE: Synthesizing Thermal Images for Facial Landmarking'.

Thermal-facial-alignment network (TFAN) trained on the T-FAKE dataset

Using the landmarker

Install and run:

pip install thermal-face-alignment

import cv2
from tfan import ThermalLandmarks

# Read a thermal image (grayscale)
image = cv2.imread("thermal.png", cv2.IMREAD_GRAYSCALE)

# Initialize landmarker (downloads weights on first use)
landmarker = ThermalLandmarks(device="cpu", n_landmarks=478)

landmarks, confidences = landmarker.process(image)

Training dataset

We trained our landmarker on our custom-made T-FAKE dataset consisting of synthetic thermal images. To download the original color images, sparse annotations, and segmentation masks for the dataset, please use the links in the FaceSynthetics repository.

Our dataset has been generated for a warm and for a cold condition. Each dataset can be downloaded separately as

• A small sample with 100 images from here warm and here cold
• A medium sample with 1,000 images from here warm and here cold
• The full dataset with 100,000 images from here warm and here cold
• The dense annotations are available from here

Pre-trained models

The models for the thermalization as well as the landmarkers can be downloaded from here.

License

Our landmarking methods and the training dataset are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International license as it is derived from the FaceSynthetics dataset.

Citation

If you use this code for your own work, please cite our paper:

P. Flotho, M. Piening, A. Kukleva and G. Steidl, “T-FAKE: Synthesizing Thermal Images for Facial Landmarking,” Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025. CVF Open Access

BibTeX entry

@InProceedings{tfake2025_CVPR,
    author    = {Flotho, Philipp and Piening, Moritz and Kukleva, Anna and Steidl, Gabriele},
    title     = {T-FAKE: Synthesizing Thermal Images for Facial Landmarking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26356-26366}
}

The thermal face bounding box detection in this repo uses the TFW landmarker model, please additionally cite:

Kuzdeuov, A., Aubakirova, D., Koishigarina, D., & Varol, H. A. (2022). TFW: Annotated Thermal Faces in the Wild Dataset. IEEE Transactions on Information Forensics and Security, 17, 2084–2094. https://doi.org/10.1109/TIFS.2022.3177949

@article{9781417,
    author={Kuzdeuov, Askat and Aubakirova, Dana and Koishigarina, Darina and Varol, Huseyin Atakan},
    journal={IEEE Transactions on Information Forensics and Security},
    title={TFW: Annotated Thermal Faces in the Wild Dataset},
    year={2022},
    volume={17},
    pages={2084-2094},
    doi={10.1109/TIFS.2022.3177949}
}