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Cited by 25Emotion and Mood RecognitionFace recognition and analysisFace Recognition and Perception

Perception of emotional expressions in different representations using facial feature points

Afzal, Shazia, Sezgin, Tevfik Metin, Gao, Yujian, Robinson, Peter·2009·2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops
DOI 10.1109/acii.2009.5349549
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Abstract

Facial expression recognition is an enabling technology for affective computing. Many existing facial expression analysis systems rely on automatically tracked facial feature points. Although psychologists have studied emotion perception from manually specified or marker-based point-light displays, no formal study exists on the amount of emotional information conveyed through automatically tracked feature points. We assess the utility of automatically extracted feature points in conveying emotions for posed and naturalistic data and present results from an experiment that compared human raters' judgements of emotional expressions between actual video clips and three automatically generated representations of them. The implications for optimal face representation and creation of realistic animations are discussed.

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Crossref · 10.1109/acii.2009.5349549

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  • Afzal et al.'s experiments confirmed that human judgement is still the best, with the highest recognition rates, followed by stick-figure models, point-light displays, and then XFace animations.

    “Their experiences confirmed that the human judgement is still the best one, with the highest recognition rates, followed by stick-figure models, point-light displays, and then XFace animations.”

    From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [48]

  • Afzal et al. performed studies on differences between human raters' judgments of emotional expressions and three automatically generated representations in video clips.

    “Afzal et al. [48] performed studies differences between human raters' judgments of emotional expressions and three automatically generated representations in video clips.”

    From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [48]

  • The three automatically generated representations were point-based display, stick figure models, and 3D XFace Animation.

    “First, the point-based display was created from the output of a commercial automatic face-tracker [49] on a black background.Second one, stick figure models, which connecting the automatically tracked feature-points using straight lines and sketching eyes using typical shape.The last one, 3D XFace Animation was a 3D animated facial expressions and displays created using XFace open source toolkit.”

    From Survey on AI-Based Multimodal Methods for Emotion Detection (2019) · cites this paper as [48]

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Related

Authors
Afzal, S., Sezgin, T. M., Gao, Y., & Robinson, P.
Affiliations
  • University of Cambridge (GB)
  • Koç University (TR)
ORCID
Venue
2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops
Publisher
IEEE
Published
2009-09
Pages
1-6
ISSN
2156-8103 (ISSN-L)
DOI
10.1109/acii.2009.5349549
OpenAlex
W2114231266
Cited by
25
References
16
Citations by year
2023: 1 · 2022: 2 · 2021: 2 · 2019: 3 · 2018: 1 · 2017: 3 · 2016: 1 · 2015: 1 · 2014: 2 · 2013: 3 · 2012: 4
FWCI
3.46
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Affective Computing and Intelligent Interaction and workshops (publishedVersion)
Funders
Gates Cambridge Trust
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
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Face Recognition and Perception
Keywords
facial expression recognition, emotion perception, facial feature points, point-light displays, face representation, affective computing, facial animation, human raters

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