Learning to Personalize in Appearance-Based Gaze Tracking
This addresses the challenge of personal variations in gaze tracking for applications like human-computer interaction, though it is incremental with a specific gain.
The paper tackles the problem of personal variations limiting appearance-based gaze tracking performance by introducing SPAZE, which models variations as a low-dimensional latent space, achieving a 2.70-degree error with 9 calibration samples on MPIIGaze and improving state-of-the-art by 14%.
Personal variations severely limit the performance of appearance-based gaze tracking. Adapting to these variations using standard neural network model adaptation methods is difficult. The problems range from overfitting, due to small amounts of training data, to underfitting, due to restrictive model architectures. We tackle these problems by introducing the SPatial Adaptive GaZe Estimator (SPAZE). By modeling personal variations as a low-dimensional latent parameter space, SPAZE provides just enough adaptability to capture the range of personal variations without being prone to overfitting. Calibrating SPAZE for a new person reduces to solving a small optimization problem. SPAZE achieves an error of 2.70 degrees with 9 calibration samples on MPIIGaze, improving on the state-of-the-art by 14 %. We contribute to gaze tracking research by empirically showing that personal variations are well-modeled as a 3-dimensional latent parameter space for each eye. We show that this low-dimensionality is expected by examining model-based approaches to gaze tracking. We also show that accurate head pose-free gaze tracking is possible.