The algorithm presented in this repo uses OpenCV, Dlib and a pre-trained CNN model called GazeML to succesfully estimate a person's point of gaze in 3D space. It does so by performing the following steps:

• Face Detection & Tracking
• Facial Landmarks Estimation
• Head Pose Estimation
• Gaze Direction Estimation
• Point of Gaze Estimation

Currently, the algorithm does not perform any calibration to compensate for different person's orientation regarding the camera, however this is planned to be implemented.

• Takes a single camera image as input (current default implementation works with Webcams)
• Outputs head pose orientation, facial and eye landmarks, gaze direction vectors, point of gaze (relative to camera origin)
• Visualizes of the outputs
  • The head orientation is displayed as cartesian coordinate system originating from the person's forehead
  • The facial and eye landmarks are displayed in orange
  • The gaze direction vectors originate form the eyeball center and are coloured in cyan
  • The point of gaze is connected to the eyeball centers via a blue arrow

• Python
• OpenCV
• Dlib
• TensorFlow

Run(with sudo appended if necessary),

 python3 setup.py install 

Tensorflow should be installed separately. Inside the setup.py there is a list of all packages installed installed in my current working virtual environment. From these you can create your own Requirements Files if you would prefer.

MIT