README.md

gaze_detect

CSCE 643 - Fall 2021 Eye gaze detection using neural networks. LeNet-based model used to train on the MPIIGAZE dataset. Head pose-independent and dependent gaze estimation is implemented.

Trained model is then utilized for webcam gaze inference using a 6-point face model and Perspective-n-Point solver.

Also see project_report.pdf.

Environment

Operating system: Ubuntu 21.04 Programming Language: Python 3.9.7

Setup

1. Clone repository git clone
2. Install requirements pip install -r requirements.txt

Run inference

1. Download Dlib trained face detection model from https://github.com/davisking/dlib-models/blob/master/shape_predictor_68_face_landmarks.dat.bz2 and place it in the assets/ directory.
2. Switch to the code directory: cd code/
3. Calibrate camera matrix python calibrate_cam.py
4. Run webcam inference code python webcam_gaze.py
   • To use head pose dependent model, modifications in utils_webcam.py's estimate_gaze and webcam_gaze.py's model definitions needed.

Train model

1. Download MPIIGAZE dataset from http://datasets.d2.mpi-inf.mpg.de/MPIIGaze/MPIIGaze.tar.gz and place the extracted directory MPIIGAZE along with its contents in thedata/ directory.
2. Switch to the code directory: cd code/
3. Set training configuration as desired in train.py
4. Train the model: python train.py
   • The trained model will overwrite the existing pretrained model in assets/models/ unless the path is changed.
   • To train other models, use the following:
     1. Head pose-dependent LeNet: python train_other_LeNet.py
     2. Head pose-dependent AlexNet: python train_other_AlexNet.py

References:

• Normalization: https://www.mpi-inf.mpg.de/departments/computer-vision-and-machine-learning/research/gaze-based-human-computer-interaction/appearance-based-gaze-estimation-in-the-wild/
• Other implementation: https://github.com/hysts/pytorch_mpiigaze/