Enhancing interpretability of gaze-tracking convolutional neural networks

Innodem Neurosciences is developing a visible light gaze-tracking algorithms that can be sued to predic a user’s gaze position on the screen of a mobile device without the need for any third-party hardware. This algorithm leverages various image processing techniques, and relies on the use of convolutional neural networks and computer vision. Enhancing the quality of this gaze prediction network will be the primary goal of the resident scientist over the course of this project. As such, the student will support Innoderm’s AI team in the interpretation and modification of our convolutional networks, help the team better tune the model’s parameters, and ultimately test and analyse the efficacy of the changes to our models.

Faculty Supervisor:

Blake Richards

Student:

Arna Ghosh

Partner:

Innodem Neurosciences

Discipline:

Computer science

Sector:

Professional, scientific and technical services

University:

McGill University