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2022 | OriginalPaper | Buchkapitel

EFG-Net: A Unified Framework for Estimating Eye Gaze and Face Gaze Simultaneously

verfasst von : Hekuangyi Che, Dongchen Zhu, Minjing Lin, Wenjun Shi, Guanghui Zhang, Hang Li, Xiaolin Zhang, Jiamao Li

Erschienen in: Pattern Recognition and Computer Vision

Verlag: Springer International Publishing

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Abstract

Gaze is of vital importance for understanding human purpose and intention. Recent works have gained tremendous progress in appearance-based gaze estimation. However, all these works deal with eye gaze estimation or face gaze estimation separately, ignoring the mutual benefit of the fact that eye gaze and face gaze are roughly the same with a slight difference in the starting point. For the first time, we propose an Eye gaze and Face Gaze Network (EFG-Net), which makes eye gaze estimation and face gaze estimation take advantage of each other, leading to a win-win situation. Our EFG-Net consists of three feature extractors, a feature communication module named GazeMixer, and three predicting heads. The GazeMixer is designed to propagate coarse gaze features from face gaze to eye gaze and fine gaze features from eye gaze to face gaze. The predicting heads are capable of estimating gazes from the corresponding features more finely and stably. Experiments show that our method achieves state-of-the-art performance of 3.90° (by \({\sim } 4 \% \)) eye gaze error and 3.93° (by \({\sim } 2 \% \)) face gaze error on MPIIFaceGaze dataset, 3.03° eye gaze error and 3.17° (by \({\sim } 5 \% \)) face gaze error on GazeCapture dataset respectively.

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Titel: EFG-Net: A Unified Framework for Estimating Eye Gaze and Face Gaze Simultaneously
verfasst von: Hekuangyi Che
Dongchen Zhu
Minjing Lin
Wenjun Shi
Guanghui Zhang
Hang Li
Xiaolin Zhang
Jiamao Li
Verlag: Springer International Publishing
Buch: Pattern Recognition and Computer Vision
Print ISBN: 978-3-031-18906-7

Electronic ISBN: 978-3-031-18907-4

Copyright-Jahr: 2022
DOI: https://doi.org/10.1007/978-3-031-18907-4_43

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