Calibration in deep-learning eye tracking

Sammanfattning: 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. In this thesis, these problems are tackled 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 \ang{2.70} with \num{9} calibration samples on MPIIGaze, improving on the state-of-the-art by \SI{14}{\percent}.In the introductory chapters the history, methods and applications of eye tracking are reviewed, with focus on video-based eye tracking and the use of personal calibration in these methods. Emphasis is placed on methods using neural networks and the strengths and weaknesses of how these methods implement personal calibration.