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2020 | OriginalPaper | Buchkapitel

Adaptive Person-Specific Appearance-Based Gaze Estimation

verfasst von : Chuanyang Zheng, Jun Zhou, Jun Sun, Lihua Zhao

Erschienen in: Digital TV and Wireless Multimedia Communication

Verlag: Springer Singapore

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Abstract

Non-invasive gaze estimation from only eye images captured by camera is a challenging problem due to various eye shapes, eye structures and image qualities. Recently, CNN network has been applied to directly regress eye image to gaze direction and obtains good performance. However, generic approaches are susceptible to bias and variance highly relating to different individuals. In this paper, we study the person-specific bias when applying generic methods on new person. And we introduce a novel appearance-based deep neural network integrating meta-learning to reduce the person-specific bias. Given only a few person-specific calibration images collected in normal calibration process, our model adapts quickly to test person and predicts more accurate gaze directions. Experiments on public MPIIGaze dataset and Eyediap dataset show our approach has achieved competitive accuracy to current state-of-the-art methods and are able to alleviate person-specific bias problem.

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Vorheriges Kapitel Attention-Based Top-Down Single-Task Action Recognition in Still Images
Nächstes Kapitel Preliminary Study on Visual Attention Maps of Experts and Nonexperts When Examining Pathological Microscopic Images
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Metadaten
Titel
Adaptive Person-Specific Appearance-Based Gaze Estimation
verfasst von
Chuanyang Zheng
Jun Zhou
Jun Sun
Lihua Zhao
Copyright-Jahr
2020
Verlag
Springer Singapore
Buch
Digital TV and Wireless Multimedia Communication

Print ISBN: 978-981-15-3340-2

Electronic ISBN: 978-981-15-3341-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-981-15-3341-9_11

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