In posed expression databases, the participants are asked to display different basic emotional expressions, while in spontaneous expression database, the expressions are natural. Spontaneous expressions differ from posed ones remarkably in terms of intensity, configuration, and duration. Apart from this, synthesis of some AUs are barely achievable without undergoing the associated emotional state. Therefore, in most cases, the posed expressions are exaggerated, while the spontaneous ones are subtle and differ in appearance.
| class="wikitable"
!Database
!Facial expression
!Number of Subjects
!Number of images/videos
!Gray/Color
!Resolution, Frame rate
!Ground truth
!Type |
FERG-3D-DB (Facial Expression Research Group 3D Database) for stylized characters [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.]
|angry, disgust, fear, joy, neutral, sad, surprise
|4
|39574 annotated examples
|Color
|
|Emotion labels
|Frontal pose |
Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) [Livingstone & Russo (2018). The Ryerson Audio-Visual Database of
]Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English. {{doi|10.1371/journal.pone.0196391
}}
|Speech: Calm, happy, sad, angry, fearful, surprise, disgust, and neutral.
Song: Calm, happy, sad, angry, fearful, and neutral.
Each expression at two levels of emotional intensity.
|24
|7356 video and audio files
|Color
|1280x720 (720p)
|Facial expression labels
Ratings provided by 319 human raters
|Posed |
Extended Cohn-Kanade Dataset (CK+)[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]
|neutral, sadness, surprise, happiness, fear, anger, contempt and disgust
|123
|593 image sequences (327 sequences having discrete emotion labels)
|Mostly gray
|640* 490
|Facial expression labels and FACS (AU label for final frame in each image sequence)
|Posed; spontaneous smiles |
Japanese Female Facial Expressions (JAFFE)[{{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}}]
|neutral, sadness, surprise, happiness, fear, anger, and disgust
|10
|213 static images
|Gray
|256* 256
|Facial expression label
|Posed |
MMI Database[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]
|
|43
|1280 videos and over 250 images
|Color
|720* 576
|AU label for the image frame with apex facial expression in each image sequence
|Posed and Spontaneous |
rowspan="3" |Belfast Database[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]
|Set 1 (disgust, fear, amusement, frustration, surprise)
|114
|570 video clips
|Color
|720*576
|
| rowspan="3" |Natural Emotion |
| Set 2 (disgust, fear, amusement, frustration, surprise, anger, sadness)
|82
|650 video clips
|Color
|
| |
| Set 3 (disgust, fear, amusement)
|60
|180 video clips
|Color
|1920*1080
| |
Indian Semi-Acted Facial Expression Database (iSAFE)[{{Cite book|last1=Singh|first1=Shivendra|last2=Benedict|first2=Shajulin|title=Advances in Signal Processing and Intelligent Recognition Systems |chapter=Indian Semi-Acted Facial Expression (ISAFE) Dataset for Human Emotions Recognition |date=2020|editor-last=Thampi|editor-first=Sabu M.|editor2-last=Hegde|editor2-first=Rajesh M.|editor3-last=Krishnan|editor3-first=Sri|editor4-last=Mukhopadhyay|editor4-first=Jayanta|editor5-last=Chaudhary|editor5-first=Vipin|editor6-last=Marques|editor6-first=Oge|editor7-last=Piramuthu|editor7-first=Selwyn|editor8-last=Corchado|editor8-first=Juan M.|chapter-url=https://link.springer.com/chapter/10.1007/978-981-15-4828-4_13|series=Communications in Computer and Information Science|volume=1209 |language=en|location=Singapore|publisher=Springer|pages=150–162|doi=10.1007/978-981-15-4828-4_13|isbn=978-981-15-4828-4}}]
|Happy, Sad, Fear, Surprise, Angry, Neutral, Disgust
|44
|395 clips
|Color
|1920x1080
(60 fps)
|Emotion labels
|Spontaneous |
DISFA[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]
| -
|27
|4,845 video frames
|Color
|1024*768; 20 fps
|AU intensity for each video frame (12 AUs)
|Spontaneous |
Multimedia Understanding Group (MUG)[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.]
|neutral, sadness, surprise, happiness, fear, anger, and disgust
|86
|1462 sequences
|Color
|896*896, 19fps
|Emotion labels
|Posed |
Indian Spontaneous Expression Database (ISED)[S L Happy, P. Patnaik, A. Routray, and R. Guha, "The Indian Spontaneous Expression Database for Emotion Recognition," in IEEE Transactions on Affective Computing, 2016, {{doi|10.1109/TAFFC.2015.2498174}}.]
|sadness, surprise, happiness, and disgust
|50
|428 videos
|Color
|1920* 1080, 50 fps
|Emotion labels
|Spontaneous |
Radboud Faces Database (RaFD)[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}}]
|neutral, sadness, contempt, surprise, happiness, fear, anger, and disgust
|67
|Three different gaze directions and five camera angles (8*67*3*5=8040 images)
|Color
|681*1024
|Emotion labels
|Posed |
| Oulu-CASIA NIR-VIS database
|surprise, happiness, sadness, anger, fear and disgust
|80
|three different illumination conditions: normal, weak and dark (total 2880 video sequences)
|Color
|320×240
|
|Posed |
FERG (Facial Expression Research Group Database)-DB[{{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}}] for stylized characters
|angry, disgust, fear, joy, neutral, sad, surprise
|6
|55767
|Color
|768x768
|Emotion labels
|Frontal pose |
AffectNet[{{Cite journal|last1=Mollahosseini|first1=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|s2cid=37515850}}]
|neutral, happy, sad, surprise, fear, disgust, anger, contempt
|
|~450,000 manually annotated
~ 500,000 automatically annotated
|Color
|Various
|Emotion labels, valence, arousal
|Wild setting |
IMPA-FACE3D[{{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}}]
|neutral frontal, joy, sadness, surprise, anger, disgust, fear, opened, closed, kiss, left side, right side, neutral sagittal left, neutral sagittal right, nape and forehead (acquired sometimes)
|38
|534 static images
|Color
|640X480
|Emotion labels
|Posed |
| FEI Face Database
|neutral,smile
|200
|2800 static images
|Color
|640X480
|Emotion labels
|Posed |
Aff-Wild[{{Cite book|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.|title=2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |chapter=Aff-Wild: Valence and Arousal 'In-the-Wild' Challenge |date=2017|chapter-url=https://eprints.mdx.ac.uk/22045/1/aff_wild_kotsia.pdf|pages=1980–1987|doi=10.1109/CVPRW.2017.248|isbn=978-1-5386-0733-6|s2cid=3107614|url=http://urn.fi/urn:nbn:fi-fe201902276466 }}][{{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|arxiv=1804.10938}}]
|valence and arousal
|200
|~1,250,000 manually annotated
|Color
|Various (average = 640x360)
|Valence, Arousal
|In-the-Wild setting |
Aff-Wild2[{{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}}][{{Cite book|last1=Kollias|first1=D.|last2=Schulc|first2=A.|last3=Hajiyev|first3=E.|last4=Zafeiriou|first4=S.|title=2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020) |chapter=Analysing Affective Behavior in the First ABAW 2020 Competition |date=2020|chapter-url=https://www.computer.org/csdl/proceedings-article/fg/2020/307900a794/1kecIYu9wL6|pages=637–643|doi=10.1109/FG47880.2020.00126|arxiv=2001.11409|isbn=978-1-7281-3079-8|s2cid=210966051}}]
|neutral, happiness, sadness, surprise, fear, disgust, anger + valence-arousal + action units 1,2,4,6,12,15,20,25
|458
|~2,800,000 manually annotated
|Color
|Various (average = 1030x630)
|Valence, Arousal, 7 basic expressions, action units for each video frame
|In-the-Wild setting |
Real-world Affective Faces Database (RAF-DB)[{{Cite web|last=Li.|first=S.|title=RAF-DB|url=http://www.whdeng.cn/RAF/model1.html|website=Real-world Affective Faces Database}}][{{Cite book|last1=Li|first1=S.|last2=Deng|first2=W.|last3=Du|first3=J.|title=2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |chapter=Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild |date=2017|chapter-url=https://ieeexplore.ieee.org/document/8099760|pages=2584–2593|doi=10.1109/CVPR.2017.277|isbn=978-1-5386-0457-1|s2cid=11413183}}]
|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)
|
|29672 annotated examples
|Color
|Various for original dataset and 100x100 for aligned dataset
|Emotion labels
|Posed and Spontaneous |