MULTIMODAL AFFECT MODELING AND RECOGNITION FOR EMPATHIC ROBOT COMPANIONS
Open accessAbstract
Affect recognition for socially perceptive robots relies on representative data. While many of the existing affective corpora and databases contain posed and decontextualized affective expressions, affect resources for designing an affect recognition system in naturalistic human–robot interaction (HRI) must include context-rich expressions that emerge in the same scenario of the final application. In this paper, we propose a context-based approach to the collection and modeling of representative data for building an affect-sensitive robotic game companion. To illustrate our approach we present the key features of the Inter-ACT (INTEracting with Robots–Affect Context Task) corpus, an affective and contextually rich multimodal video corpus containing affective expressions of children playing chess with an iCat robot. We show how this corpus can be successfully used to train a context-sensitive affect recognition system (a valence detector) for a robotic game companion. Finally, we demonstrate how the integration of the affect recognition system in a modular platform for adaptive HRI makes the interaction with the robot more engaging.
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Context (situation, interacting persons, place etc) can also improve emotion inference.
“Context (situation, interacting persons, place etc) can also improve emotion inference [271], [272].”
From Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications (2016) · cites this paper as [271]
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References 38 · Cited by 51
- Authors
- CASTELLANO, G., LEITE, I., PEREIRA, A., MARTINHO, C., PAIVA, A., & MCOWAN, P. W.
- Affiliations
- University of Birmingham (GB)
- Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT)
- Queen Mary University of London (GB)
- Venue
- International Journal of Humanoid Robotics
- Publisher
- World Scientific Pub Co Pte Lt
- Published
- 2013-03
- Volume
- 10
- Issue
- 01
- Pages
- 1350010
- ISSN
- 0219-8436 (ISSN-L)
- 1793-6942
- OpenAlex
- W2126554711
- Cited by
- 51
- References
- 38
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 5 · 2020: 6 · 2019: 2 · 2018: 6 · 2017: 6 · 2016: 3 · 2015: 4 · 2014: 6 · 2013: 3
- FWCI
- 7.22
- Type
- journal article
- Language
- en
- Open access
- green
- zenodo.org
- Licence
- other-oa
- Locations
- International Journal of Humanoid Robotics (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, other-oa)
- Funders
- Fundação para a Ciência e a Tecnologia
- FP7 Information and Communication Technologies
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- affect recognition, human-robot interaction
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