Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"
Open accessAbstract
Computer classification of facial expressions requires large amounts of data and this data needs to reflect the diversity of conditions seen in real applications. Public datasets help accelerate the progress of research by providing researchers with a benchmark resource. We present a comprehensively labeled dataset of ecologically valid spontaneous facial responses recorded in natural settings over the Internet. To collect the data, online viewers watched one of three intentionally amusing Super Bowl commercials and were simultaneously filmed using their webcam. They answered three self-report questions about their experience. A subset of viewers additionally gave consent for their data to be shared publicly with other researchers. This subset consists of 242 facial videos (168, 359 frames) recorded in real world conditions. The dataset is comprehensively labeled for the following: 1) frame-by-frame labels for the presence of 10 symmetrical FACS action units, 4 asymmetric (unilateral) FACS action units, 2 head movements, smile, general expressiveness, feature tracker fails and gender, 2) the location of 22 automatically detected landmark points, 3) self-report responses of familiarity with, liking of, and desire to watch again for the stimuli videos and 4) baseline performance of detection algorithms on this dataset. This data is available for distribution to researchers online, the EULA can be found at: http://www.affectiva.com/facial-expression-dataset-am-fed/.
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AMFED contains spontaneous FEs recorded in natural settings over the Internet.
“AMFED contains spontaneous FEs recorded in natural settings over the Internet.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [204]
While previous RGB datasets record FEs in controlled lab environments, AFEW, AMFED and SEMAINE contain faces in naturalistic environments.
“While previous RGB datasets record FEs in controlled lab environments, Acted Facial Expressions In The Wild Database (AFEW) [203], Affectiva-MIT Facial Expression Dataset (AMFED) [204] and SEMAINE [205] contain faces in naturalistic environments.”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [204]
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Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584 · see citing works
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Self-citationMcKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schroder, M. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.20 · Cited by 665 · see citing works
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AFEW-VA database for valence and arousal estimation in-the-wild
Kossaifi, J., Tzimiropoulos, G., Todorovic, S., & Pantic, M. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.02.001 · Cited by 236 · see citing works
Video-based emotion recognition in the wild using deep transfer learning and score fusion
Kaya, H., Gürpınar, F., & Salah, A. A. (2017) · Image and Vision Computing · doi:10.1016/j.imavis.2017.01.012 · Cited by 235 · see citing works
Automatic Detection of Learning-Centered Affective States in the Wild
Bosch, N., D'Mello, S., Baker, R., Ocumpaugh, J., Shute, V., Ventura, M., Wang, L., & Zhao, W. (2015) · Proceedings of the 20th International Conference on Intelligent User Interfaces · doi:10.1145/2678025.2701397 · Cited by 178 · see citing works
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My Bad! Repairing Intelligent Voice Assistant Errors Improves Interaction
Cuadra, A., Li, S., Lee, H., Cho, J., & Ju, W. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449101 · Cited by 52 · see citing works
Automatic measurement of ad preferences from facial responses gathered over the Internet
McDuff, D., El Kaliouby, R., Senechal, T., Demirdjian, D., & Picard, R. (2014) · Image and Vision Computing · doi:10.1016/j.imavis.2014.01.004 · Cited by 52 · see citing works
Self-citation
- Authors
- McDuff, D., el Kaliouby, R., Senechal, T., Amr, M., Cohn, J. F., & Picard, R.
- Affiliations
- MIT Media Lab (US)
- Affectiva (United States) (US)
- Carnegie Mellon University (US)
- Venue
- 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops
- Publisher
- IEEE
- Published
- 2013-06
- Pages
- 881-888
- OpenAlex
- W2008933718
- Cited by
- 234
- References
- 3
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 18 · 2023: 14 · 2022: 17 · 2021: 21 · 2020: 18 · 2019: 26 · 2018: 30 · 2017: 28 · 2016: 23 · 2015: 17 · 2014: 7 · 2013: 1
- FWCI
- 21.77
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- cc-by-nc-sa
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- Funders
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- facial expressions, spontaneous facial expressions, in-the-wild data, naturalistic video, facial landmark detection, webcam video, self-report, Super Bowl commercials, affective computing, emotion recognition
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