|
|Posed
|-
|Aff-Wild<ref>{{Cite journalbook|last1=Zafeiriou|first1=S.|last2=Kollias|first2=D.|last3=Nicolaou|first3=M.A.|last4=Papaioannou|first4=A.|last5=Zhao|first5=G.|last6=Kotsia|first6=I.|datetitle=2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |titlechapter=Aff-Wild: Valence and Arousal in'In-the-wildWild' Challenge |date=2017|chapter-url=httphttps://openaccesseprints.thecvfmdx.comac.uk/content_cvpr_2017_workshops22045/w331/papers/Zafeiriou_Aff-Wild_Valence_and_CVPR_2017_paperaff_wild_kotsia.pdf|journal=Computer Vision and Pattern Recognition Workshops (CVPRW), 2017|pages=1980–1987|doi=10.1109/CVPRW.2017.248|isbn=978-1-5386-0733-6|s2cid=3107614}}</ref><ref>{{Cite journal|last1=Kollias|first1=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 |volume=127|issue=6–7|pages=907–929|doi=10.1007/s11263-019-01158-4|s2cid=13679040|doi-access=free}}</ref>
|valence and arousal
|200
|In-the-Wild setting
|-
|Aff-Wild2<ref>{{Cite journal|last1=Kollias|first1=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|arxiv=1910.04855}}</ref><ref>{{Cite journalbook|last1=Kollias|first1=D.|last2=Schulc|first2=A.|last3=Hajiyev|first3=E.|last4=Zafeiriou|first4=S.|datetitle=2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) |titlechapter=Analysing affectiveAffective behaviorBehavior in the firstFirst abawABAW 2020 competitionCompetition |date=2020|chapter-url=https://www.computer.org/csdl/proceedings-article/fg/2020/307900a794/1kecIYu9wL6|journal=IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2020|pages=637–643|doi=10.1109/FG47880.2020.00126|arxiv=2001.11409|isbn=978-1-7281-3079-8|s2cid=210966051}}</ref>
|neutral, happiness, sadness, surprise, fear, disgust, anger + valence-arousal + action units 1,2,4,6,12,15,20,25
|458
|In-the-Wild setting
|-
|Real-world Affective Faces Database (RAF-DB)<ref>{{Cite web|last=Li.|first=S.|title=RAF-DB|url=http://www.whdeng.cn/RAF/model1.html|website=Real-world Affective Faces Database}}</ref><ref>{{Cite journalbook|last1=Li|first1=S.|last2=Deng|first2=W.|last3=Du|first3=J.|datetitle=2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |titlechapter=Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild |date=2017|chapter-url=https://ieeexplore.ieee.org/document/8099760|journal=2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)|pages=2584–2593|doi=10.1109/CVPR.2017.277|isbn=978-1-5386-0457-1|s2cid=11413183}}</ref>
|6 classes of '''basic emotions''' (Surprised, Fear, Disgust, Happy, Sad, Angry) plus Neutral and 12 classes of '''compound emotions''' (Fearfully Surprised, Fearfully Disgusted, Sadly Angry, Sadly Fearful, Angrily Disgusted, Angrily Surprised, Sadly Disgusted, Disgustedly Surprised, Happily Surprised, Sadly Surprised, Fearfully Angry, Happily Disgusted)
|
|
