FaceTube: predicting personality from facial expressions of emotion in online conversational video
Abstract
The advances in automatic facial expression recognition make possible to mine and characterize large amounts of data, opening a wide research domain on behavioral understanding. In this paper, we leverage the use of a state-of-the-art facial expression recognition technology to characterize users of a popular type of online social video, conversational vlogs. First, we propose the use of several activity cues to characterize vloggers based on frame-by-frame estimates of facial expressions of emotion. Then, we present results for the task of automatically predicting vloggers' personality impressions using facial expressions and the Big-Five traits. Our results are promising, specially for the case of the Extraversion impression, and in addition our work poses interesting questions regarding the representation of multiple natural facial expressions occurring in conversational video.
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The dominant approach for personality detection is to use multimodality by combining acoustic with visual cues or physiological with visual cues.
“While there are works on detecting personality and mood from FEs only [251], [252] the dominant approach is to use multimodality either by combining acoustic with visual cues [251], [257] or physiological with visual cues [258].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [251]
While many studies have showed improvements of prediction when combined with physiological or acoustic cues, facial expressions remain marginal in the study of personality trait prediction.
“Practically though, while many studies have showed improvements of prediction when combined with physiological or acoustic cues, FEs remain marginal in the study of personality trait prediction [251], [257], [259], [260].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [251]
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Mawalim, C. O., Okada, S., & Nakano, Y. (2021) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3450283 · Cited by 2 · see citing works
A Survey on Image Acquisition Protocols for Non-posed Facial Expression Recognition Systems
Saha, P., Bhattacharjee, D., De, B. K., & Nasipuri, M. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7596-2 · Cited by 2 · see citing works
A Cultural Comparison of the Facial Inference Process
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Automated Domain Bias Correction and Its Application in Text-Based Personality Analysis
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Automatic Analysis of Facilitated Taste-liking
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Zero-shot Personality Perception From Facial Images
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Emotional Expression as a Means of Communicating Virtual Human Personalities
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An Iterative Approach to Build a Semantic Dataset for Facial Expression of Personality
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Multimodal personality assessment from audio, visual, and verbal features
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- Authors
- Biel, J.-I., Teijeiro-Mosquera, L., & Gatica-Perez, D.
- Affiliations
- École Polytechnique Fédérale de Lausanne (CH)
- Universidade de Vigo (ES)
- Ecole Polytechnique Fédérale de Lausanne, Martigny, Switzerland
- Venue
- Proceedings of the 14th ACM international conference on Multimodal interaction
- Publisher
- ACM
- Published
- 2012-10-22
- Pages
- 53-56
- OpenAlex
- W2133269263
- Cited by
- 104
- References
- 14
- Citations by year
- 2026: 6 · 2025: 1 · 2024: 8 · 2023: 6 · 2022: 6 · 2021: 8 · 2020: 9 · 2019: 13 · 2018: 4 · 2017: 11 · 2016: 17 · 2015: 3 · 2014: 8 · 2013: 4
- FWCI
- 9.28
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION (publishedVersion)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
- University of California, San Diego
- 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 expression recognition, vlogs, personality prediction, Big Five personality traits, extraversion, conversational video, emotion recognition
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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