The DOI matches its Crossref record; 115 of the 115 references it cites were found in a registry or an index.
Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives
Open accessAbstract
A fascinating challenge in the field of human–robot interaction is the possibility to endow robots with emotional intelligence in order to make the interaction more intuitive, genuine, and natural. To achieve this, a critical point is the capability of the robot to infer and interpret human emotions. Emotion recognition has been widely explored in the broader fields of human–machine interaction and affective computing. Here, we report recent advances in emotion recognition, with particular regard to the human–robot interaction context. Our aim is to review the state of the art of currently adopted emotional models, interaction modalities, and classification strategies and offer our point of view on future developments and critical issues. We focus on facial expressions, body poses and kinematics, voice, brain activity, and peripheral physiological responses, also providing a list of available datasets containing data from these modalities.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Rapid object detection using a boosted cascade of simple features
Viola, P., & Jones, M. (2005) · Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001 · doi:10.1109/cvpr.2001.990517 · Cited by 18,300
Russell, J. A. (1980) · Journal of Personality and Social Psychology · doi:10.1037/h0077714 · Cited by 16,334 · cited 1×
“Conversely, several dimensional annotated datasets share a common valence-arousal (VA) (Russell, 1980) representation, which allows to compare and merge data from different datasets, in the worst case by ignoring additional axes.”
BackgroundProceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · Proceedings of IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1993.341010 · Cited by 14,265
Proceedings of IEEE International Conference on Computer Vision
(1995) · Proceedings of IEEE International Conference on Computer Vision · doi:10.1109/iccv.1995.466933 · Cited by 12,860
Salovey, P., & Mayer, J. D. (1990) · Imagination, Cognition and Personality · doi:10.2190/dugg-p24e-52wk-6cdg · Cited by 10,484
An argument for basic emotions
Ekman, P. (1992) · Cognition and Emotion · doi:10.1080/02699939208411068 · Cited by 9,646
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., & Patras, I. (2012) · IEEE Transactions on Affective Computing · doi:10.1109/t-affc.2011.15 · Cited by 5,093
Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops · doi:10.1109/cvprw.2010.5543262 · Cited by 4,303
Autonomic nervous system activity in emotion: a review
Kreibig, S. D. (2010) · Biological Psychology · doi:10.1016/j.biopsycho.2010.03.010 · Cited by 3,188 · cited 1×
“Emotions affect body physiology, producing significant modifications to hearth rate, blood volume pressure (BVP), respiration, skin conductivity, and temperature (Kreibig, 2010), which can contribute to predict the emotional state of a person.”
BackgroundA survey of socially interactive robots
Fong, T., Nourbakhsh, I., & Dautenhahn, K. (2003) · Robotics and Autonomous Systems · doi:10.1016/s0921-8890(02)00372-x · Cited by 3,162 · cited 1×
“…and Cerezo, 2019); • Emotional expression of robots: in complex interaction scenarios, such as assistive, educational, and social robotics (Fong et al., 2003; Rossi et al., 2020), the ability of robots to exhibit recognizable emotional expressions strongly impacts the resulting social…”
Background
Cited by
Advanced Applications of Industrial Robotics: New Trends and Possibilities
Dzedzickis, A., Subačiūtė-Žemaitienė, J., Šutinys, E., Samukaitė-Bubnienė, U., & Bučinskas, V. (2021) · Applied Sciences · doi:10.3390/app12010135 · Cited by 228
Multimodal emotion recognition: A comprehensive review, trends, and challenges
Ramaswamy, M. P. A., & Palaniswamy, S. (2024) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1563 · Cited by 70
Digital loneliness—changes of social recognition through AI companions
Jacobs, K. A. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1281037 · Cited by 48
State-of-the-art Multimodal Emotion Recognition: A comprehensive survey and taxonomy
Yazici, A., Kucukyilmaz, T., Dokeroglu, T., Sharipbay, A., Lee, M.-H., & Tyler, B. (2026) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2026.200642 · Cited by 11
Lu, C.-K., Lu, C.-W., & Bo Lin, G. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3621086 · Cited by 4
Metadata
- Authors
- Spezialetti, M., Placidi, G., & Rossi, S.
- Venue
- Frontiers in Robotics and AI
- Publisher
- Frontiers Media SA
- Published
- 2020-12-21
- Volume
- 7
- Pages
- 532279-532279
- ISSN
- 2296-9144 (ISSN-L)
- OpenAlex
- W3114195756
- Cited by
- 293
- References
- 115
- FWCI
- 24.29
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives. https://app.cochat.ai/papers/10.3389/frobt.2020.532279
- The paper
- Spezialetti, M., Placidi, G., & Rossi, S. (2020). Emotion Recognition for Human-Robot Interaction: Recent Advances and Future Perspectives. Frontiers in Robotics and AI. https://doi.org/10.3389/frobt.2020.532279
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
