Face Tells Detailed Expression: Generating Comprehensive Facial Expression Sentence Through Facial Action Units

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Human facial expression plays the key role in the understanding of the social behavior. Many deep learning approaches present facial emotion recognition and automatic image captioning considering human sentiments. However, most current deep learning models for facial expression analysis do not contain comprehensive, detailed information of a single face. In this paper, we newly introduce a text-based facial expression description using several essential components describing comprehensive facial expression: gender, facial action units, and corresponding intensities. Then, we propose comprehensive facial expression sentence generating model along with facial expression recognition model for a single facial image to verify the effectiveness of our text-based dataset. Experimental results show that the proposed two models are supporting each other improving their performances: the text-based facial expression description provides comprehensive semantic information to the facial emotion recognition model. Also, the visual information from the emotion recognition model guides the facial expression sentence generation to produce a proper sentence describing comprehensive description. The text-based dataset is available at https://github.com/joannahong/Textbased- dataset- with-comprehensive-facial-expression- sentence.
Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
Issue Date
2020-01-07
Language
English
Citation

26th International Conference on MultiMedia Modeling (MMM), pp.100 - 111

ISSN
0302-9743
DOI
10.1007/978-3-030-37734-2_9
URI
http://hdl.handle.net/10203/288306
Appears in Collection
EE-Conference Papers(학술회의논문)
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