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


Eye gaze is an important non-verbal cue and promising for automatic affect analysis. We propose an appearance-based method that, in contrast to a long 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 particularly pronounced for the most challenging extreme head poses.

MPIIFaceGaze Dataset

The MPIIFaceGaze dataset is based on the MPIIGaze dataset, with the additional human facial landmark annotation and the face regions are avaliable.


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

Download: You can download the original 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.

Contact: Xucong Zhang, Campus E1.4 room 609, E-mail:

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

  • It’s Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation, X. Zhang, Y. Sugano, M. Fritz and A. Bulling, Proc. of the IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), 2017.
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  • @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},


- Data
There are 15 participants and corresponding folder from p00 to p14. Each folder includes the images inside the different day folder.

- Annotation
There are pxx.txt file in each participant folder. which saves the information:Dimension 1: image file path and name.
Dimension 2~3: Gaze location on the screen coordinate in pixels, the actual screen size can be found in the "Calibration" folder.
Dimension 4~15: (x,y) position for the six facial landmarks, which are four eye corners and two mouth corners.
Dimension 16~21: The estimated 3D head pose in the camera coordinate system based on 6 points-based 3D face model, rotation and translation: we implement the same 6 points-based 3D face model in [4], which includes the four eye corners and two mouth corners
Dimension 22~24: The gaze direction in the camera coordinate system.
Dimension 25: Which eye (left or right) is used for the evlauaiton subset in [2].

- Calibration
There is the "Calibration" folder for each participant, which contains
(1)Camera.mat: the intrinsic parameter of the laptop camera. "cameraMatrix": the projection matrix of the camera. "distCoeffs": camera distortion coefficients. "retval": root mean square (RMS) re-projection error. "rvecs": the rotation vectors. "tvecs": the translation vectors.
(2) monitorPose.mat: the position of image plane in camera coordinate. "rvecs": the rotation vectors. "tvecs": the translation vectors.
(3) creenSize.mat: the laptop screen size. "height_pixel": the screen height in pixel. "width_pixel": the screen width in pixel. "height_mm": the screen height in millimeter. "width_mm": the screen width in millimeter.

Full-face Patch Gaze Estimation


We proposed a full-face patch gaze estimation method with novel spatial weights CNN. To efficiently use the information from full-face images, we propose to use additional layers that learn spatial weights for the activation of the last convolutional layer. we used the concept of the three 1 × 1 convolutional layers plus rectified linear unit layers to generate a single heatmap encoding the importance across the whole face image. We then performed an element-wise multiplication of this weight map with the feature map of the previous convolutional layer.

The source code is available here, the pre-trained caffe model is available here. Please note this is still beta version, and bug report and suggestion are welcome.


2D Gaze Estimation

We did the leave-one-person-out corss-calidation for our MPIIFaceGaze dataset, and 5-fold corss-validataion for EYEDIAP dataset [3]. The restls are shown in the below figures. The left axis shows the Euclidean error between estimated and ground-truth gaze positions in the screen coordinate system in millimetres. The right axis shows the corresponding angular error that was approxi- mately calculated from the camera and monitor calibration information provided by the dataset and the same reference position for the 3D gaze estimation task.

For MPIIGaze dataset (left), the proposed spatial weights network achieved a statistically significant 7.2% performance improvement (paired t-test: p < 0.01) over the second best single face model. These findings are in general mirrored for the EYEDIAP dataset (right), while the overall performance is worse most likely due to the lower resolution and the limited amount of training images.

3D Gaze Estimation

As the same setting for our MPIIFaceGaze dataset and EYEIDAP dataset, we conducted the 3D gaze estimation evaluaitons.

The left axis shows the angular error that was directly calculated from the estimated and ground-truth 3D gaze vectors. The right axis shows the corresponding Euclidean error that was approximated by intersecting the esti- mated 3D gaze vector with the screen plane. 

For our MPIIFaceGaze dataset (left), our proposed model achieved a significant performance improvement of 14.3% (paired t-test: p > 0.01) over iTracker, and a performance consistent with the 2D case. For EYEDIAP dataset (right), the proposed model also achieved the best performance for the 3D gaze estimation task on the EYEDIAP dataset.


[1] Eye Tracking for Everyone. K.Krafka*, A. Khosla*, P. Kellnhofer, H. Kannan, S. Bhandarkar, W. Matusik and A. Torralba
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

[2] Appearance-based Gaze Estimation in the Wild, X. Zhang, Y. Sugano, M. Fritz and A. Bulling, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

[3] EYEDIAP: A Database for the Development and Evaluation of Gaze Estimation Algorithms from RGB and RGB-D Cameras. Funes Mora, Kenneth Alberto and Monay, Florent and Odobez, Jean-Marc. Proceedings of the ACM Symposium on Eye Tracking Research and Applications (ETRA), 2014.

[4] Learning-by-synthesis for appearance-based 3d gaze estimation. Y. Sugano, Y. Matsushita, and Y. Sato. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.