Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is n...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 38; číslo 8; s. 1548 - 1568
Hlavní autori:	Corneanu, C. A., Oliu Simon, Marc, Cohn, Jeffrey F., Escalera Guerrero, Sergio
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2016 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	affect Algorithms Emotion recognition Encoding Face Facial Expression Humans Iron multimodal Pattern Recognition, Automated RGB Taxonomy thermal Three-dimensional displays
ISSN:	0162-8828, 2160-9292, 1939-3539
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Abstract	Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.
AbstractList	Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.
Author	Cohn, Jeffrey F. Escalera Guerrero, Sergio Corneanu, C. A. Oliu Simon, Marc
Author_xml	– sequence: 1 givenname: C. A. surname: Corneanu fullname: Corneanu, C. A. email: cipriancorneanu@ub.edu organization: Comput. Vision Center, Univ. Autonoma de Barcelona, Barcelona, Spain – sequence: 2 givenname: Marc surname: Oliu Simon fullname: Oliu Simon, Marc email: moliusimon@gmail.com organization: Comput. Vision Center, Univ. Autonoma de Barcelona, Barcelona, Spain – sequence: 3 givenname: Jeffrey F. surname: Cohn fullname: Cohn, Jeffrey F. email: jeffcohn@cs.cmu.edu organization: Robot. Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA – sequence: 4 givenname: Sergio surname: Escalera Guerrero fullname: Escalera Guerrero, Sergio email: sergio@maia.ub.es organization: Comput. Vision Center, Univ. Autonoma de Barcelona, Barcelona, Spain
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26761193$$D View this record in MEDLINE/PubMed
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Snippet	Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from...
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SubjectTerms	affect Algorithms Emotion recognition Encoding Face Facial Expression Humans Iron multimodal Pattern Recognition, Automated RGB Taxonomy thermal Three-dimensional displays
Title	Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications
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