Person-Specific Gaze Estimation for Unconstrained Real-World Interactions
Valentin Venzin · Repository for Publications and Research Data (ETH Zurich) · 2020
Eye gaze is a promising input modality for attentive user interfaces and interactive applications on various devices.Appearance-based methods estimate the gaze direction from a monocular RGB camera and can therefore easily be deployed on unmodified devices.However, the accuracy of appearance-based methods is limited by person-specific anatomical variation.Personal calibration is therefore necessary to achieve practical gaze estimation accuracy for real-world applications.Depending on the use case, collecting personal calibration samples could be impractical or a tedious task for the user.In this thesis, we propose a novel method to improve few-shot person-specific gaze estimation in unconstrained environments.We leverage gaze redirection as a way to augment personal calibration data and learn a multilayer perceptron to map person-independent gaze predictions to more accurate, person-specific gaze directions.Our method achieves promising results in a cross-device setting with unreliable calibration data which was collected without the user's active collaboration.Our method improves the person-independent gaze estimation error by 17.9% with only two calibration samples on mobile phones, thus improving accuracy while keeping the cost of collecting calibration data low.Finally, we propose a novel filter to remove unreliable samples from implicit calibration data.This filter further improves the gaze estimation accuracy in some scenarios, paving the way for a more user-friendly integration of eye gaze into interactive systems.i I would especially like to thank my supervisors Dr. Xucong Zhang and Mihai Bâce for introducing me to this project at the intersection of my research interests: Deep Learning and Human Computer Interaction.Their guidance, suggestions and feedback had a very positive impact on the outcome of this work.Thank you also to Seonwook Park for the helpful discussions and for providing crucial insight regarding the topic of gaze redirection.I want to thank Prof. Dr. Otmar Hilliges for making this project possible and for reviewing this thesis as an examiner.A big 'thank you' also to the members of the AIT lab for being welcoming and for providing a generous workplace with ample resources.I am grateful to my parents, Maria and Gabriel