Abstract
In this work, we present GazeGraph, a system that leverages human gazes as the sensing modality for cognitive context sensing. GazeGraph is a generalized framework that is compatible with different eye trackers and supports various gaze-based sensing applications. It ensures high sensing performance in the presence of heterogeneity of human visual behavior, and enables quick system adaptation to unseen sensing scenarios with few-shot instances. To achieve these capabilities, we introduce the spatial-temporal gaze graphs and the deep learning-based representation learning method to extract powerful and generalized features from the eye movements for context sensing. Furthermore, we develop a few-shot gaze graph learning module that adapts the `learning to learn' concept from meta-learning to enable quick system adaptation in a data-efficient manner. Our evaluation demonstrates that GazeGraph outperforms the existing solutions in recognition accuracy by 45% on average over three datasets. Moreover, in few-shot learning scenarios, GazeGraph outperforms the transfer learning-based approach by 19% to 30%, while reducing the system adaptation time by 80%.
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