Title: A Neural Network Gaze Estimator

Project Description

This project focuses on training a neural network (NN) to track a user’s eye movement and classify their gaze direction and eye status. The model will take input from a camera and determine whether a user is blinking, looking forward, left, right, down, or up.

To achieve this, a dataset of eye images will be collected and labeled, ensuring diversity across individuals, lighting conditions, and head positions. The trained NN will then be tested in real-time on different cameras to evaluate its accuracy across various users and conditions.

This research builds on my previous work in assistive robotics, where I explored gaze-based control for navigation however without using any ML. The ability to accurately estimate gaze direction could further enable hands-free control in assistive technologies and human-computer interaction applications.

Project Goals

1. Create a dataset containing images from multiple individuals, capturing different gaze directions and eye statuses under varied conditions.
2. Train a neural network to accurately classify eye movement directions and blinking states.
3. Test the model across different cameras and user environments to verify general condition.
4. Optimize performance to ensure real-time responsiveness.