Cited by 234Emotion and Mood RecognitionFace recognition and analysisFace and Expression Recognition

Affectiva-MIT Facial Expression Dataset (AM-FED): Naturalistic and Spontaneous Facial Expressions Collected "In-the-Wild"

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McDuff, Daniel, el Kaliouby, Rana, Senechal, Thibaud, Amr, May, Cohn, Jeffrey F., Picard, Rosalind·2013·2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

Abstract

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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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
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
Google
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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