Learning Representations for Facial Actions From Unlabeled Videos

Facial actions are usually encoded as anatomy-based action units (AUs), the labelling of which demands expertise and thus is time-consuming and expensive. To alleviate the labelling demand, we propose to leverage the large number of unlabelled videos by proposing a twin-cycle autoencoder (TAE) to le...

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Bibliographic Details
Published in:	IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 1; pp. 302 - 317
Main Authors:	Li, Yong, Zeng, Jiabei, Shan, Shiguang
Format:	Journal Article
Language:	English
Published:	United States IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Decoupling Displacement encoder-decoder structure Face Facial action unit detection feature disentanglement Feature extraction Gold Head Head movement Image management Image quality Image reconstruction Image retrieval Labelling Learning Magnetic heads Movement representation learning Representations self-supervised learning Task analysis Video Videos
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
Online Access:	Get full text
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