MPIIFaceGaze: It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation

We present the MPIIFaceGaze dataset which is based on the MPIIGaze dataset, with the additional human facial landmark annotation and the face regions are available. We added additional facial landmark and pupil center annotations for 37,667 face images. Facial landmarks annotations were conducted in a semi-automatic manner as running facial landmark detection method first and then checking by two human annotators. The pupil centers were annotated by two human annotators from scratch. For sake of privacy, we only released the face region and blocked the background in images.

More information can be found here.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License .

Download: Please download the full dataset here (940 Mb).

You can also download the normalized data from here, which includes the normalized face images as 448*448 pixels size and 2D gaze angle vectors. Please note that the data normalization procedure changed the gaze direction label so that you need to convert results based on this normalized data to the original camera space.

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

   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. 

   It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation

   Xucong Zhang, Yusuke Sugano, Mario Fritz, Andreas Bulling

   Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2299-2308, 2017.

   Abstract Links BibTeX Project

   Eye gaze is an important non-verbal cue for human affect analysis. Recent gaze estimation work indicated that information from the full face region can benefit performance. Pushing this idea further, we propose an appearance-based method that, in contrast to a long-standing line of work in computer vision, only takes the full face image as input. Our method encodes the face image using a convolutional neural network with spatial weights applied on the feature maps to flexibly suppress or enhance information in different facial regions. Through extensive evaluation, we show that our full-face method significantly outperforms the state of the art for both 2D and 3D gaze estimation, achieving improvements of up to 14.3% on MPIIGaze and 27.7% on EYEDIAP for person-independent 3D gaze estimation. We further show that this improvement is consistent across different illumination conditions and gaze directions and par- ticularly pronounced for the most challenging extreme head poses.

   doi:10.1109/CVPRW.2017.284

   zhang17_cvprw.pdf

   @inproceedings{zhang17_cvprw, 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 = {Proc. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)}, year = {2017}, doi = {10.1109/CVPRW.2017.284}, pages = {2299-2308} }