Deep Learning based Eye Tracking on Smartphones for Dynamic Visual Stimuli
Nishan Gunawardena, Jeewani Anupama Ginige, Bahman Javadi, Gough Yumu Lui · Procedia Computer Science · 2024
Performing human gaze estimation using smartphones is invaluable in human-computer interaction with various potential appli- cations, ranging from user interface enhancements to medical research. We developed three deep learning-based mobile device eye-tracking architectures for dynamic visual stimuli. This includes a combination of Convolutional Neural Networks (CNN) with two different Recurrent Neural Networks (RNN), namely Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). Our CNN+LSTM and CNN+GRU models achieved an average Root Mean Square Error of 0.955cm and 1.091cm, respectively. The codes and models are available on https://github.com/NishNilanka/MobileEye.git .