MPIIGaze: Appearance-Based Gaze Estimation in the Wild

We present the MPIIGaze dataset that contains 213,659 images that we collected from 15 participants during natural everyday laptop use over more than three months. The number of images collected by each participant varied from 34,745 to 1,498. Our dataset is significantly more variable than existing ones with respect to appearance and illumination.

The dataset contains three parts: “Data”, “Evaluation Subset” and “Annotation subset”.

* The “Data” includes “Original”, “Normalized” and “Calibration” for all the 15 participants.
* The “Evaluation Subset” contains the image list that indicates the selected samples for the evaluation subset in our paper.
* The “Annotation Subset” contains the image list that indicates 10,848 samples that we manually annotated, following the annotations with (x, y) position of 6 facial landmarks (four eye corners, two mouth corners) and (x,y) position of two pupil centers for each of above images.

More information can be found here.

Download: Please download the full dataset here (2.1 Gb).

Contact: Andreas Bulling,

The data is only to be used for non-commercial scientific purposes. If you use this dataset in a scientific publication, please cite the following paper:

1. 

   MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation

   Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling

   IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 41(1), pp. 162-175, 2019.

   Abstract Links BibTeX Project

   Learning-based methods are believed to work well for unconstrained gaze estimation, i.e. gaze estimation from a monocular RGB camera without assumptions regarding user, environment, or camera. However, current gaze datasets were collected under laboratory conditions and methods were not evaluated across multiple datasets. Our work makes three contributions towards addressing these limitations. First, we present the MPIIGaze dataset, which contains 213,659 full face images and corresponding ground-truth gaze positions collected from 15 users during everyday laptop use over several months. An experience sampling approach ensured continuous gaze and head poses and realistic variation in eye appearance and illumination. To facilitate cross-dataset evaluations, 37,667 images were manually annotated with eye corners, mouth corners, and pupil centres. Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze. We study key challenges including target gaze range, illumination conditions, and facial appearance variation. We show that image resolution and the use of both eyes affect gaze estimation performance, while head pose and pupil centre information are less informative. Finally, we propose GazeNet, the first deep appearance-based gaze estimation method. GazeNet improves on the state of the art by 22% (from a mean error of 13.9 degrees to 10.8 degrees) for the most challenging cross-dataset evaluation.

   doi: 10.1109/TPAMI.2017.2778103

   Paper: zhang19_pami.pdf

   @article{zhang19_pami, title = {MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation}, author = {Zhang, Xucong and Sugano, Yusuke and Fritz, Mario and Bulling, Andreas}, year = {2019}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)}, doi = {10.1109/TPAMI.2017.2778103}, pages = {162-175}, volume = {41}, number = {1} }

1. 

   Appearance-based Gaze Estimation in the Wild

   Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling

   Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4511-4520, 2015.

   Abstract Links BibTeX Project

   Appearance-based gaze estimation is believed to work well in real-world settings but existing datasets were collected under controlled laboratory conditions and methods were not evaluated across multiple datasets. In this work we study appearance-based gaze estimation in the wild. We present the MPIIGaze dataset that contains 213,659 images we collected from 15 participants during natural everyday laptop use over more than three months. Our dataset is significantly more variable than existing datasets with respect to appearance and illumination. We also present a method for in-the-wild appearance-based gaze estimation using multimodal convolutional neural networks, which significantly outperforms state-of-the art methods in the most challenging cross-dataset evaluation setting. We present an extensive evaluation of several state-of-the-art image-based gaze estimation algorithm on three current datasets, including our own. This evaluation provides clear insights and allows us identify key research challenges of gaze estimation in the wild.

   doi: 10.1109/CVPR.2015.7299081

   Paper: zhang15_cvpr.pdf

   Video: https://www.youtube.com/watch?v=rw6LZA1USG8

   @inproceedings{zhang15_cvpr, author = {Zhang, Xucong and Sugano, Yusuke and Fritz, Mario and Bulling, Andreas}, title = {Appearance-based Gaze Estimation in the Wild}, booktitle = {Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year = {2015}, pages = {4511-4520}, doi = {10.1109/CVPR.2015.7299081}, video = {https://www.youtube.com/watch?v=rw6LZA1USG8} }