Datasets

This page provides the dataset-related information of 2D gaze estimation.

In this page,

  • we introduce the data pre-processing of each datasets.
  • we provide the code of data pre-processing.
  • we summarize the links of datasets.

We invite you to use our data processing code, and add your result into the benchmark.

Note that, you should first download the gaze rectification code data_processing_core.py from this page before using our data processing code.

If you use the codes in this page, please cite our survey:

@article{Cheng2021Survey,
    title={Appearance-based Gaze Estimation With Deep Learning: A Review and Benchmark},
    author={Yihua Cheng and Haofei Wang and Yiwei Bao and Feng Lu},
    journal={arXiv preprint arXiv:2104.12668},
    year={2021}
}

GazeCapture

Data pre-processing

We follow the evaluation protocol of GazeCapture. They already split the whole dataset into training, test and validation set. We crop the face and eye images based on the provided annotation. Note that, the cropped image maybe have different size. You need to resize them into suitable size in your code.

Codes of pre-processing

Please download here.

The code contains following parameters.

root = "/home/cyh/dataset/Original/GazeCapture"
out_root = "/home/cyh/dataset/FaceBased/GazeCapture"
  • The root is the path of original GazeCapture dataset.
  • The out_root is the path for saving result file.

To use the code, you should first set the two paramters, and run

python data_processing_gazecapture.py

We also provide the guide for using our normalized data.

The original dataset can be downloaded from here.

To apply our code on this original dataset, please also cite:

@InProceedings{Krafka_2016_CVPR,
    author = {Krafka, Kyle and Khosla, Aditya and Kellnhofer, Petr and Kannan, Harini and Bhandarkar, Suchendra and Matusik, Wojciech and Torralba, Antonio},
    title = {Eye Tracking for Everyone},
    booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {June},
    year = {2016}
}

MPIIFaceGaze

This dataset provides face and eye images. It also provides the PoG.

Data pre-processing

We follow the original protocol to split dataset. A total of 15 subjects and 3000 images of each subject is used for evaluation. We crop the face and eye images, and directly use the provided PoR in MPIIGaze.

Codes of pre-processing

Please download here. The code contains following parameters.

root = "/home/cyh/dataset/Original/MPIIFaceGaze"
sample_root = "/home/cyh/dataset/Original/MPIIGaze/Origin/Evaluation Subset/sample list for eye image"
out_root = "/home/cyh/dataset/GazePoint/MPIIGaze"
  • The root is the path of MPIIFaceGaze.
  • The sample_root indicate the sample list in MPIIGaze. Note that, this file is not contained in MPIIFaceGaze. You should download MPIIGaze for this file.
  • The out_root is the path for saving result file.

To use the code, you should set the three parameters first., and run

python data_processing_mpii.py

The processed data is saved in out_root.

We also provide the guide for using our normalized data.

The original dataset can be downloaded from here.

To apply our code on this original dataset, please also cite:

@inproceedings{zhang2017s,
    title={It’s written all over your face: Full-face appearance-based gaze estimation},
    author={Zhang, Xucong and Sugano, Yusuke and Fritz, Mario and Bulling, Andreas},
    booktitle={Computer Vision and Pattern Recognition Workshops (CVPRW), 2017 IEEE Conference on},
    pages={2299--2308},
    year={2017},
    organization={IEEE}
}

EyeDiap

This dataset provides face and eye images. We also provide the PoR.

Data pre-processing

We select VGA videos of screen targets and sample one image per fifteen frames. We then truncate the sampled image of each video to ensure the same number of images. We crop the face and eye images from each frame, and use the provided PoR. In our benchmark, we randomly divide whole subjects into four clusters and perform four-fold validation.

Codes of pre-processing

Please download here. The code contains following parameters.

root = "/home/cyh/dataset/Original/EyeDiap/Data"
out_root = "/home/cyh/dataset/EyeBased/EyeDiap"
  • The root is the path of original EyeDiap dataset.
  • The out_root is the path for saving result file.

To use the code, you should first set the two paramters, and run

python data_processing_diap.py

The normalized data is saved in out_root. We also provide the guide for using our normalized data.

please download here for cluster_diap.py.

The original dataset can be downloaded from here.

To apply our code on this original dataset, please also cite:

@inproceedings{eyediap,
    author = {Funes Mora, Kenneth Alberto and Monay, Florent and Odobez, Jean-Marc},
    title = {EYEDIAP: A Database for the Development and Evaluation of Gaze Estimation Algorithms from RGB and RGB-D Cameras},
    year = {2014},
    isbn = {9781450327510},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/2578153.2578190},
    doi = {10.1145/2578153.2578190},
    booktitle = {Proceedings of the Symposium on Eye Tracking Research and Applications},
    pages = {255–258},
    numpages = {4},
    keywords = {natural-light, database, RGB-D, RGB, remote sensing, gaze estimation, depth, head pose},
    location = {Safety Harbor, Florida},
    series = {ETRA '14}
}