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!Ground truth
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|FERG-3D-DB (Facial Expression Research Group 3D Database) for stylized characters <ref>Aneja, Deepali, et al. "Learning to generate 3D stylized character expressions from humans." 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018.</ref>
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|Emotion labels
|Frontal pose
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|Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) <ref>Livingstone & Russo (2018). The Ryerson Audio-Visual Database of
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Ratings provided by 319 human raters
|Posed
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|Extended Cohn-Kanade Dataset (CK+)<ref>P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar and I. Matthews, "The Extended Cohn-Kanade Dataset (CK+): A complete facial expression dataset for action unit and emotion-specified expression," in ''3rd IEEE Workshop on CVPR for Human Communicative Behavior Analysis'', 2010</ref>
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|Facial expression labels and FACS (AU label for final frame in each image sequence)
|Posed; spontaneous smiles
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|Japanese Female Facial Expressions (JAFFE)<ref>{{Cite book | doi=10.5281/zenodo.3451524| year=1998| last1=Lyons| first1=Michael| title=The Japanese Female Facial Expression (JAFFE) Database| last2=Kamachi| first2=Miyuki| last3=Gyoba| first3=Jiro}}</ref>
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|Facial expression label
|Posed
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|MMI Database<ref>M. Valstar and M. Pantic, "Induced disgust, happiness and surprise: an addition to the MMI facial expression database," in ''Proc. Int. Conf. Language Resources and Evaluation'', 2010</ref>
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|AU label for the image frame with apex facial expression in each image sequence
|Posed and Spontaneous
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| rowspan="3" |Belfast Database<ref>I. Sneddon, M. McRorie, G. McKeown and J. Hanratty, "The Belfast induced natural emotion database," ''IEEE Trans. Affective Computing,'' vol. 3, no. 1, pp. 32-41, 2012</ref>
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| rowspan="3" |Natural Emotion
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|Set 2 (disgust, fear, amusement, frustration, surprise, anger, sadness)
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|650 video clips
|Color
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|Color
|1920*1080
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|Emotion labels
|Spontaneous
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|DISFA<ref>S. M. Mavadati, M. H. Mahoor, K. Bartlett, P. Trinh and J. Cohn., "DISFA: A Spontaneous Facial Action Intensity Database," ''IEEE Trans. Affective Computing,'' vol. 4, no. 2, pp. 151–160, 2013</ref>
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|AU intensity for each video frame (12 AUs)
|Spontaneous
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|Multimedia Understanding Group (MUG)<ref>N. Aifanti, C. Papachristou and A. Delopoulos, The MUG Facial Expression Database, in Proc. 11th Int. Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS), Desenzano, Italy, April 12–14, 2010.</ref>
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|Emotion labels
|Posed
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|Indian Spontaneous Expression Database (ISED)<ref>S L Happy, P. Patnaik, A. Routray, and R. Guha,
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|Emotion labels
|Spontaneous
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|Radboud Faces Database (RaFD)<ref>Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H.J., Hawk, S.T., & van Knippenberg, A. (2010). Presentation and validation of the Radboud Faces Database. Cognition & Emotion, 24(8), 1377—1388. {{doi|10.1080/02699930903485076}}</ref>
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|Emotion labels
|Posed
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|Oulu-CASIA NIR-VIS database
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|Posed
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|FERG (Facial Expression Research Group Database)-DB<ref>{{Cite web|url=http://grail.cs.washington.edu/projects/deepexpr/ferg-db.html|title=Facial Expression Research Group Database (FERG-DB)|website=grail.cs.washington.edu|access-date=2016-12-06}}</ref> for stylized characters
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|Emotion labels
|Frontal pose
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|AffectNet<ref>{{Cite journal|last=Mollahosseini|first=A.|last2=Hasani|first2=B.|last3=Mahoor|first3=M. H.|date=2017|title=AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild|journal=IEEE Transactions on Affective Computing|volume=PP|issue=99|pages=18–31|doi=10.1109/TAFFC.2017.2740923|issn=1949-3045|arxiv=1708.03985}}</ref>
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|Emotion labels, valence, arousal
|Wild setting
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|IMPA-FACE3D<ref>{{Cite web|url=http://app.visgraf.impa.br/database/faces/documents/|title=IMPA-FACE3D Technical Reports|website=visgraf.impa.br|access-date=2018-03-08}}</ref>
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|Emotion labels
|Posed
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|FEI Face Database
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|Emotion labels
|Posed
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|Aff-Wild<ref>{{Cite journal|last=Zafeiriou|first=S.|last2=Kollias|first2=D.|last3=Nicolaou|first3=M.A.|last4=Papaioannou|first4=A.|last5=Zhao|first5=G.|last6=Kotsia|first6=I.|date=2017|title=Aff-Wild: Valence and Arousal in-the-wild Challenge|url=http://openaccess.thecvf.com/content_cvpr_2017_workshops/w33/papers/Zafeiriou_Aff-Wild_Valence_and_CVPR_2017_paper.pdf|journal=Computer Vision and Pattern Recognition Workshops (CVPRW), 2017}}</ref> <ref>{{Cite journal|last=Kollias|first=D.|last2=Tzirakis|first2=P.|last3=Nicolaou|first3=M.A.|last4=Papaioannou|first4=A.|last5=Zhao|first5=G.|last6=Schuller|first6=B.|last7=Kotsia|first7=I.|last8=Zafeiriou|first8=S.|date=2019|title=Deep Affect Prediction in-the-wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond|url=https://rdcu.be/bmGm2|journal=International Journal of Computer Vision (IJCV), 2019}}</ref>
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|Valence, Arousal
|In-the-Wild setting
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|Aff-Wild2<ref>{{Cite journal|last=Kollias|first=D.|last2=Zafeiriou|first2=S.|date=2019|title=Expression, affect, action unit recognition: Aff-wild2, multi-task learning and arcface|url=https://bmvc2019.org/wp-content/uploads/papers/0399-paper.pdf|journal=British Machine Vision Conference (BMVC), 2019}}</ref> <ref>{{Cite journal|last=Kollias|first=D.|last2=Schulc|first2=A.|last3=Hajiyev|first3=E.|last4=Zafeiriou|first4=S.|date=2020|title=Analysing affective behavior in the first abaw 2020 competition|url=https://www.computer.org/csdl/proceedings-article/fg/2020/307900a794/1kecIYu9wL6|journal=IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2020}}</ref>
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|Valence, Arousal, 7 basic expressions, action units for each video frame
|In-the-Wild setting
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