Zero Shot Learning in Pupil Detection
Wolfgang Fuhl · 2024
In eye tracking, pupil detection is a crucial step for gaze estimation. While there are a plethora of datasets with accurate annotations, new devices, such as differently placed cameras, usually require more data to be annotated since the new perspective is not part of the datasets so far. The research community has already published multiple simple simulators for data generation, as well as rendering-based approaches for the human eye. We created a dataset with different camera perspectives along with different challenges and evaluated the pupil simulators as well as the rendering-based approaches for zero-shot pupil detection. In our evaluation, we highlight the limitations of the simulators and the rendering-based approaches in terms of the different challenges.