To download the 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
Coming soon.
The models for the thermalization as well as the landmarkers can be downloaded from here.
Our baseline U-Net translation model is imported from segmentation_models_pytorch library. Specifically, we define the translator as follows:
import segmentation_models_pytorch as smp
translator = smp.Unet(
encoder_name="resnet34",
encoder_weights="imagenet",
in_channels=3,
classes=1,
activation="sigmoid"
)This model is based on a U-Net architecture with a ResNet-34 encoder pre-trained on ImageNet. It takes three-channel RGB input images and outputs a single-channel thermal image with a sigmoid activation function. For training progress of the thermalization model see ThermalizationCode/ThermalizerOutput.ipynb.
Will be added soon.
To run the benchmark, you have to download the CHARLOTTE ThermalFace dataset.
This dataset and the landmarking methods are licensed under the Attribution-NonCommercial-ShareAlike 4.0 International license as it is derived from the FaceSynthetics dataset.
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,” arxiv, 2024. doi:10.48550/arXiv.2408.15127
BibTeX entry
@article{flotea2024a,
author = {Flotho, P. and Piening, M. and Kukleva, A. and Steidl, G.},
title = {T-FAKE: Synthesizing Thermal Images for Facial Landmarking},
year = {2024},
journal = {arXiv preprint arXiv:2408.15127},
doi = {https://doi.org/10.48550/arXiv.2408.15127}
}