Abstract
Recent advances in eye tracking have given birth to a new genre of gaze-based context sensing applications, ranging from cognitive load estimation to emotion recognition. To achieve state-of-the-art recognition accuracy, a large-scale, labeled eye movement dataset is needed to train deep learning-based classifiers. However, due to the heterogeneity in human visual behavior, as well as the labor-intensive and privacy-compromising data collection process, datasets for gaze-based activity recognition are scarce and hard to collect. To alleviate the sparse gaze data problem, we present EyeSyn, a novel suite of psychology-inspired generative models that leverages only publicly available images and videos to synthesize a realistic and arbitrarily large eye movement dataset. Taking gaze-based museum activity recognition as a case study, our evaluation demonstrates that EyeSyn can not only replicate the distinct pat-terns in the actual gaze signals that are captured by an eye tracking device, but also simulate the signal diversity that results from dif-ferent measurement setups and subject heterogeneity. Moreover, in the few-shot learning scenario, EyeSyn can be readily incorpo-rated with either transfer learning or meta-learning to achieve 90% accuracy, without the need for a large-scale dataset for training.
Original language | English |
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Title of host publication | Proceedings of the 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) |
Editors | L. O'Conner |
Place of Publication | Piscataway |
Publisher | IEEE |
Pages | 233-246 |
Number of pages | 14 |
ISBN (Electronic) | 978-1-6654-9624-7 |
ISBN (Print) | 978-1-6654-9625-4 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) - Milano, Italy Duration: 4 May 2022 → 6 May 2022 Conference number: 21st |
Conference
Conference | 2022 21st ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) |
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Abbreviated title | IPSN 2022 |
Country/Territory | Italy |
City | Milano |
Period | 4/05/22 → 6/05/22 |
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-careOtherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Keywords
- Eye tracking
- eye movement synthesis
- activity recognition