CASIA OpenIR  > 模式识别国家重点实验室  > 生物识别与安全技术
Dominant and Complementary Emotion Recognition From Still Images of Faces
Guo, Jianzhu1,2; Lei, Zhen1,2; Wan, Jun1,2; Avots, Egils3; Hajarolasvadi, Noushin4; Knyazev, Boris5; Kuharenko, Artem5; Silveira Jacques Junior, Julio C.6,7; Baro, Xavier6,7; Demirel, Hasan4; Escalera, Sergio7,8; Allik, Jueri9; Anbarjafari, Gholamreza3,10
Source PublicationIEEE ACCESS
ISSN2169-3536
2018
Volume6Pages:26391-26403
Corresponding AuthorWan, Jun(jun.wan@nlpr.ia.ac.cn) ; Escalera, Sergio(sergio@maia.ub.es) ; Anbarjafari, Gholamreza(shb@ut.ee)
AbstractEmotion recognition has a key role in affective computing. Recently, fine-grained emotion analysis, such as compound facial expression of emotions, has attracted high interest of researchers working on affective computing. A compound facial emotion includes dominant and complementary emotions (e.g., happily-disgusted and sadly-fearful), which is more detailed than the seven classical facial emotions (e.g., happy, disgust, and so on). Current studies on compound emotions are limited to use data sets with limited number of categories and unbalanced data distributions, with labels obtained automatically by machine learning-based algorithms which could lead to inaccuracies. To address these problems, we released the iCV-MEFED data set, which includes 50 classes of compound emotions and labels assessed by psychologists. The task is challenging due to high similarities of compound facial emotions from different categories. In addition, we have organized a challenge based on the proposed iCV-MEFED data set, held at FG workshop 2017. In this paper, we analyze the top three winner methods and perform further detailed experiments on the proposed data set. Experiments indicate that pairs of compound emotion (e.g., surprisingly-happy vs happily-surprised) are more difficult to be recognized if compared with the seven basic emotions. However, we hope the proposed data set can help to pave the way for further research on compound facial emotion recognition.
KeywordDominant and complementary emotion recognition compound emotions fine-grained face emotion dataset
DOI10.1109/ACCESS.2018.2831927
WOS KeywordFACIAL EXPRESSION RECOGNITION ; BASIC EMOTIONS ; PERCEPTION ; EYES
Indexed BySCI
Language英语
Funding ProjectEstonian Research Council[PUT638] ; Estonian Research Council[IUT213] ; Estonian Center of Excellence in IT through the European Regional Development Fund ; Spanish projects (MINECO/FEDER, UE)[TIN2015-66951-C2-2-R] ; Spanish projects (MINECO/FEDER, UE)[TIN2016-74946-P] ; CERCA Programme / Generalitat de Catalunya ; European Commission Horizon 2020 granted project SEE.4C[H2020-ICT-2015] ; CERCA Programme/Generalitat de Catalunya ; National Key Research and Development Plan[2016YFC0801002] ; Chinese National Natural Science Foundation[61502491] ; Chinese National Natural Science Foundation[61473291] ; Chinese National Natural Science Foundation[61572501] ; Chinese National Natural Science Foundation[61572536] ; Chinese National Natural Science Foundation[61673052] ; Chinese National Natural Science Foundation[61773392] ; Chinese National Natural Science Foundation[61403405] ; Scientific and Technological Research Council of Turkey (TAIJBArTAK) 1001 Project[116E097] ; Estonian Research Council[PUT638] ; Estonian Research Council[IUT213] ; Estonian Center of Excellence in IT through the European Regional Development Fund ; Spanish projects (MINECO/FEDER, UE)[TIN2015-66951-C2-2-R] ; Spanish projects (MINECO/FEDER, UE)[TIN2016-74946-P] ; CERCA Programme / Generalitat de Catalunya ; European Commission Horizon 2020 granted project SEE.4C[H2020-ICT-2015] ; CERCA Programme/Generalitat de Catalunya ; National Key Research and Development Plan[2016YFC0801002] ; Chinese National Natural Science Foundation[61502491] ; Chinese National Natural Science Foundation[61473291] ; Chinese National Natural Science Foundation[61572501] ; Chinese National Natural Science Foundation[61572536] ; Chinese National Natural Science Foundation[61673052] ; Chinese National Natural Science Foundation[61773392] ; Chinese National Natural Science Foundation[61403405] ; Scientific and Technological Research Council of Turkey (TAIJBArTAK) 1001 Project[116E097]
Funding OrganizationEstonian Research Council ; Estonian Center of Excellence in IT through the European Regional Development Fund ; Spanish projects (MINECO/FEDER, UE) ; CERCA Programme / Generalitat de Catalunya ; European Commission Horizon 2020 granted project SEE.4C ; CERCA Programme/Generalitat de Catalunya ; National Key Research and Development Plan ; Chinese National Natural Science Foundation ; Scientific and Technological Research Council of Turkey (TAIJBArTAK) 1001 Project
WOS Research AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000434935200001
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:19[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21704
Collection模式识别国家重点实验室_生物识别与安全技术
Corresponding AuthorWan, Jun; Escalera, Sergio; Anbarjafari, Gholamreza
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.Univ Tartu, Inst Technol, iCV Res Lab, EE-50411 Tartu, Estonia
4.Eastern Mediterranean Univ, Dept Elect & Elect Engn, Mersin 10, Gazimagusa, Turkey
5.NTechLab, Moscow 123056, Russia
6.Univ Oberta Catalunya, Barcelona 08018, Spain
7.Comp Vis Ctr, Barcelona 08193, Spain
8.Univ Barcelona, Barcelona 08007, Spain
9.Univ Tartu, Inst Psychol, EE-50090 Tartu, Estonia
10.Hasan Kalyoncu Univ, Dept Elect & Elect Engn, TR-27410 Gaziantep, Turkey
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Guo, Jianzhu,Lei, Zhen,Wan, Jun,et al. Dominant and Complementary Emotion Recognition From Still Images of Faces[J]. IEEE ACCESS,2018,6:26391-26403.
APA Guo, Jianzhu.,Lei, Zhen.,Wan, Jun.,Avots, Egils.,Hajarolasvadi, Noushin.,...&Anbarjafari, Gholamreza.(2018).Dominant and Complementary Emotion Recognition From Still Images of Faces.IEEE ACCESS,6,26391-26403.
MLA Guo, Jianzhu,et al."Dominant and Complementary Emotion Recognition From Still Images of Faces".IEEE ACCESS 6(2018):26391-26403.
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