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Here is a demo program.See alsothis repo.

• Linux (Tested on Ubuntu only)
• Python >= 3.7

pip install -r requirements.txt

bash scripts/download_mpiigaze_dataset.shpython tools/preprocess_mpiigaze.py --dataset datasets/MPIIGaze -o datasets/

bash scripts/download_mpiifacegaze_dataset.shpython tools/preprocess_mpiifacegaze.py --dataset datasets/MPIIFaceGaze_normalized -o datasets/

This repository usesYACS forconfiguration management.Default parameters are specified ingaze_estimation/config/defaults.py(which is not supposed to be modified directly).You can overwrite those default parameters using a YAML file likeconfigs/mpiigaze/lenet_train.yaml.

By running the following code, you can train a model using all thedata except the person with ID 0, and run test on that person.

python train.py --config configs/mpiigaze/lenet_train.yamlpython evaluate.py --config configs/mpiigaze/lenet_eval.yaml

Usingscripts/run_all_mpiigaze_lenet.sh andscripts/run_all_mpiigaze_resnet_preact.sh,you can run all training and evaluation for LeNet and ResNet-8 withdefault parameters.

The training time is the value when using GTX 1080Ti.

The training time is the value when using GTX 1080Ti.

This demo program runs gaze estimation on the video from a webcam.

1. Download the dlib pretrained model for landmark detection.

   bash scripts/download_dlib_model.sh

2. Calibrate the camera.

   Save the calibration result in the same format as the samplefiledata/calib/sample_params.yaml.

3. Run demo.

   Specify the model path and the path of the camera calibration resultsin the configuration file as inconfigs/demo_mpiigaze_resnet.yaml.

   python demo.py --config configs/demo_mpiigaze_resnet.yaml

• https://github.com/hysts/pl_gaze_estimation
• https://github.com/hysts/pytorch_mpiigaze_demo

• Zhang, Xucong, Yusuke Sugano, Mario Fritz, and Andreas Bulling. "Appearance-based Gaze Estimation in the Wild." Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.arXiv:1504.02863,Project Page
• Zhang, Xucong, Yusuke Sugano, Mario Fritz, and Andreas Bulling. "It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation." Proc. of the IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW), 2017.arXiv:1611.08860,Project Page
• Zhang, Xucong, Yusuke Sugano, Mario Fritz, and Andreas Bulling. "MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation." IEEE transactions on pattern analysis and machine intelligence 41 (2017).arXiv:1711.09017
• Zhang, Xucong, Yusuke Sugano, and Andreas Bulling. "Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications." Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), 2019.arXiv,code