Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures
Abstract
This paper presents a system for inferring complex mental states from video of facial expressions and head gestures in real-time. The system is based on a multi-level dynamic Bayesian network classifier which models complex mental states as a number of interacting facial and head displays, identified from component-based facial features. Experimental results for 6 mental states groups- agreement, concentrating, disagreement, interested, thinking and unsure are reported. Real-time performance, unobtrusiveness and lack of preprocessing make our system particularly suitable for user-independent human computer interaction.
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Most of AFER was used for predicting primary affective states of basic emotions, but FEs were also used for predicting non-primary affective states such as complex mental states, fatigue, frustration, pain, depression, mood and personality traits.
“Most of AFER was used for predicting primary affective states of basic emotions, such as anger or happiness but FEs were also used for predicting non-primary affective states such as complex mental states [223], fatigue [224], frustration [44], pain [185], [186], [225], depression [53], [250], mood and personality traits [251], [252].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [223]
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References 43 · Cited by 282
- Authors
- El Kaliouby, R., & Robinson, P.
- Affiliations
- University of Cambridge (GB)
- ORCID
- Robinson, P.
- Venue
- 2004 Conference on Computer Vision and Pattern Recognition Workshop
- Publisher
- IEEE
- Published
- 2005-04-01
- Pages
- 154-154
- OpenAlex
- W2105198535
- Cited by
- 282
- References
- 43
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 2 · 2021: 8 · 2020: 10 · 2019: 15 · 2018: 8 · 2017: 16 · 2016: 14 · 2015: 23 · 2014: 27 · 2013: 20 · 2012: 22
- Type
- conference abstract
- Language
- en
- Open access
- closed
- Locations
- IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (publishedVersion)
- Funders
- University of Cambridge
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- mental state inference, facial expressions, head gestures, dynamic Bayesian networks, real-time emotion recognition, agreement and disagreement
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