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Jul 19, 2018



UPDATE: End-to-end gaze estimation code now available, including facial landmarks detection and head pose estimation within the pipeline. Check TestOnline below.


This is the implementation of the "Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues" paper, accepted for BMVC 2018.

Trained weights using EYEDIAP dataset are available for the following models: NFEL5836, NFEL5836_2918 and NFEL5836GRU. Models used in the paper, divided into folds, are available on demand due to their size.

Main files

All the information is included inside the files, but here is a brief summary:

generate_training_CS and generate_training_FT:

Scripts to generate data files from EYEDIAP Continuous Screen target and Floating Target subsets, respectively, so that the input data is compatible with Train and Test scripts. The EYEDIAP dataset is required. If using another dataset, these scripts have to be changed accordingly. The scripts also load the 3D landmarks computed using Bulat et al. 2017 code. Examples of the format of the input 3D landmarks and generated output files can be found in the Examples folder.


Main script to train (and evaluate) the gaze estimation models included in the paper, using the files generated from generate_training_X script.

Usage example for NFEL5836GRU model:

python3 -t FT_SM_NFEL5836GRU_fold3 -exp NFEL5836GRU -dp 0.3 -bs 8 -aug 1
-lr 0.0001 -epochs 21 -gt "/Work/EYEDIAP/Annotations_final_exps/gt_cam_FT_S.txt" "/Work/EYEDIAP/Annotations_final_exps/gt_cam_FT_M.txt"
-vgt "/Work/EYEDIAP/Annotations_final_exps/gtv_cam_FT_S.txt" "/Work/EYEDIAP/Annotations_final_exps/gtv_cam_FT_M.txt"
-data "/Work/EYEDIAP/Annotations_final_exps/data_FT_S.txt" "/Work/EYEDIAP/Annotations_final_exps/data_FT_M.txt"
-feats "/Work/EYEDIAP/Annotations_final_exps/face_features_FT_S.txt" "/Work/EYEDIAP/Annotations_final_exps/face_features_FT_M.txt"
-test 2_A_FT_S 2_A_FT_M 3_A_FT_S 3_A_FT_M 8_A_FT_S 8_A_FT_M 16_A_FT_S 16_A_FT_M 16_B_FT_S 16_B_FT_M 
-p "/Work"


Main script to test the trained gaze estimation models and visualize the predicted outputs. We include a test scenario with images and calibration file in the Test folder. In this case, we use Openface to compute 3D information of the subject wrt the camera coordinate system (head pose and 3D landmarks to compute eye centers), which can be found in CCS_3D_info file. 3D landmarks used to train the model are, again, computed using Bulat et al. code.

Usage example for NFEL5836 model:

python3 -exp NFEL5836_2918 -data C:\path\Test\data.txt 
-info C:\path\Test\CCS_3D_info.txt -lndmk C:\path\Test\landmarks.txt 
-cal C:\path\Test\calibration.txt -p C:\path\


Main script to perform end-to-end gaze estimation. It loads all necessary third-party modules (OpenFace, Bulat et al face alignment, etc), and predicts gaze for all input images.


The code depends on OpenFaceWrapper, a shared library to use OpenFace within python applications. Check the link for install instructions for Windows and Unix environments.

To install keras_vggface, please use the following command:

pip install keras_vggface --no-dependencies 

If you don't add the --no-dependencies flag it will install tensorflow (no gpu version) and other dependencies, which will most likely break your whole environment :).


The code was tested using the following versions:

  • Python=3.6
  • tensorflow=1.11
  • keras=2.2.4
  • keras_vggface=0.5
  • numpy=1.16.2
  • cv2=4.1.0

EYEDIAP folds distribution

In the paper, we evaluate the cross-subject 3D gaze estimation task by means of N-fold cross-validation. The different folds are detailed below for each target type.

Floating target

Num. fold Num. subjects Folders
1 4 '5_A_FT_S', '5_A_FT_M', '10_A_FT_S', '10_A_FT_M', '11_A_FT_S', '11_A_FT_M','14_A_FT_S', '14_A_FT_M', '14_B_FT_S', '14_B_FT_M'
2 4 '1_A_FT_S', '1_A_FT_M', '4_A_FT_S', '4_A_FT_M', '6_A_FT_S', '6_A_FT_M', '15_A_FT_S', '15_A_FT_M', '15_B_FT_S', '15_B_FT_M'
3 4 '2_A_FT_S', '2_A_FT_M', '3_A_FT_S', '3_A_FT_M', '8_A_FT_S', '8_A_FT_M', '16_A_FT_S', '16_A_FT_M', '16_B_FT_S', '16_B_FT_M'
4 4 '7_A_FT_S', '7_A_FT_M', '9_A_FT_S', '9_A_FT_M', '12_B_FT_S', '12_B_FT_M', '13_B_FT_S', '13_B_FT_M'

Continuous screen target

Num. fold Num. subjects Folders
1 3 '7_A_CS_S', '7_A_CS_M', '10_A_CS_S', '10_A_CS_M', '15_A_CS_S', '15_A_CS_M'
2 3 '2_A_CS_S', '2_A_CS_M', '4_A_CS_S', '4_A_CS_M', '8_A_CS_S', '8_A_CS_M'
3 3 '3_A_CS_S', '3_A_CS_M', '6_A_CS_S', '6_A_CS_M', '16_A_CS_S', '16_A_CS_M'
4 3 '1_A_CS_S', '1_A_CS_M', '5_A_CS_S', '5_A_CS_M', '9_A_CS_S', '9_A_CS_M'
5 2 '14_A_CS_S', '14_A_CS_M', '11_A_CS_S', '11_A_CS_M'


If you find any bugs or have any comments or suggestions please contact me on Github or feel free to open an Issue. All contributions are welcomed!


If you find this code useful, please cite the following paper:

Palmero, C., Selva, J., Bagheri, M. A., & Escalera, S. Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues. Proc. of British Machine Vision Conference (BMVC), 2018.


Keras implementation of "Recurrent CNN for 3D Gaze Estimation using Appearance and Shape Cues" paper





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