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. Author manuscript; available in PMC: 2023 Oct 19.
Published in final edited form as: Proc IEEE Inst Electr Electron Eng. 2023 May 23;111(10):1236–1286. doi: 10.1109/JPROC.2023.3273517

Table 1.

Recent Representative Datasets for Emotion Recognition

Dataset Name Labeled Samples Data Type Categorical Emotions Continuous Emotions Lab Controlled Year Primary Application
IAPS [45] 1.2k I - VAD 2005 Evoked
FI [71] 23.3k I 8§ - 2016 Evoked
VideoEmotion-8 [72] 1.2k V 8 - 2014 Evoked
Ekman-6 [73] 1.6k V 6 - 2018 Evoked
E-Walk [74] 1k V 4 - 2019 BEEU
BoLD [51] 13k V 26 VAD 2020 BEEU
iMiGUE [75] 0.4k V 2 - 2021 BEEU
CK+ [76] 0.6k V 7 - 2010 FER
Aff-Wild [77] 0.3k V - VA 2017 FER
AffectNet [78] 450k I 7 - 2017 FER
EMOTIC [79] 34k I 26 VAD 2017 FER*
AFEW 8.0 [80] 1.8k V 7 - 2018 FER
CAER [81] 13k V 7 - 2019 FER*
DFEW [82] 16k V 7 - 2020 FER
FERV39k [83] 39k V 7 - 2022 FER
SAMM [84] 0.2k V 7 - 2016 MER
CAS(ME)2 [85] 0.06k V 4 - 2017 MER
ICT-MMMO [86] 0.4k V,A,T - Sentiment 2013 Multi-Modal
MOSEI [87] 23.5k V.A.T 6 Sentiment 2018 Multi-Modal

A superset of Ekman’s basic emotions

Ekman’s basic emotions + neutral

§

Mikels’ emotions

Micro-gesture understanding and emotion analysis dataset Data Type Key: (I)mage, (V)ideo, (A)udio, (T)ext

*

Context-aware emotion dataset

Data Type Key: (I)mage, (V)ideo, (A)udio, (T)ext