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Article: From Facial Expression Recognition to Interpersonal Relation Prediction

TitleFrom Facial Expression Recognition to Interpersonal Relation Prediction
Authors
KeywordsDeep convolutional network
Facial expression recognition
Interpersonal relation
Issue Date2018
Citation
International Journal of Computer Vision, 2018, v. 126, n. 5, p. 550-569 How to Cite?
Abstract© 2017, Springer Science+Business Media, LLC, part of Springer Nature. Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.
Persistent Identifierhttp://hdl.handle.net/10722/273730
ISSN
2023 Impact Factor: 11.6
2023 SCImago Journal Rankings: 6.668
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, Zhanpeng-
dc.contributor.authorLuo, Ping-
dc.contributor.authorLoy, Chen Change-
dc.contributor.authorTang, Xiaoou-
dc.date.accessioned2019-08-12T09:56:29Z-
dc.date.available2019-08-12T09:56:29Z-
dc.date.issued2018-
dc.identifier.citationInternational Journal of Computer Vision, 2018, v. 126, n. 5, p. 550-569-
dc.identifier.issn0920-5691-
dc.identifier.urihttp://hdl.handle.net/10722/273730-
dc.description.abstract© 2017, Springer Science+Business Media, LLC, part of Springer Nature. Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.-
dc.languageeng-
dc.relation.ispartofInternational Journal of Computer Vision-
dc.subjectDeep convolutional network-
dc.subjectFacial expression recognition-
dc.subjectInterpersonal relation-
dc.titleFrom Facial Expression Recognition to Interpersonal Relation Prediction-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s11263-017-1055-1-
dc.identifier.scopuseid_2-s2.0-85035130905-
dc.identifier.volume126-
dc.identifier.issue5-
dc.identifier.spage550-
dc.identifier.epage569-
dc.identifier.eissn1573-1405-
dc.identifier.isiWOS:000427289200006-
dc.identifier.issnl0920-5691-

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