Categorical and dimensional affect analysis in continuous input: Current trends and future directions
Open accessAbstract
In the context of affective human behavior analysis, we use the term continuous input to refer to naturalistic settings where explicit or implicit input from the subject is continuously available, where in a human-human or human-computer interaction setting, the subject plays the role of a producer of the communicative behavior or the role of a recipient of the communicative behavior. As a result, the analysis and the response provided by the automatic system are also envisioned to be continuous over the course of time, within the boundaries of digital machine output. The term continuous affect analysis is used as analysis that is continuous in time as well as analysis that uses affect phenomenon represented in dimensional space. The former refers to acquiring and processing long unsegmented recordings for detection of an affective state or event (e.g., nod, laughter, pain), and the latter refers to prediction of an affect dimension (e.g., valence, arousal, power). In line with the Special Issue on Affect Analysis in Continuous Input, this survey paper aims to put the continuity aspect of affect under the spotlight by investigating the current trends and provide guidance towards possible future directions.
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Given the nature of sensor data in typical HCI scenarios, dimensional representations and combinations of categorical and dimensional models of affect will become even more relevant as a more natural way of dealing with continuous data.
“However, given the nature of sensor data in typical HCI scenarios, it can be expected that dimensional representations, and combinations of categorical and dimensional models of affect will become even more relevant as a more natural way of dealing with continuous data [53].”
From Deep learning for human affect recognition: Insights and new developments (2021) · cites this paper as [53]
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- Authors
- Gunes, H., & Schuller, B.
- Affiliations
- Technical University of Munich (DE)
- ORCID
- Schuller, Björn
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 31
- Issue
- 2
- Pages
- 120-136
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W2025905516
- Semantic Scholar
- 11196dbd0b6493bcbf438e2adf38de42a74e347b
- Cited by
- 360
- References
- 200
- Citations by year
- 2026: 10 · 2025: 16 · 2024: 27 · 2023: 29 · 2022: 24 · 2021: 19 · 2020: 21 · 2019: 27 · 2018: 30 · 2017: 50 · 2016: 37 · 2015: 31 · 2014: 20 · 2013: 18 · 2012: 1
- FWCI
- 30.00
- Type
- journal article
- Language
- en
- Open access
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Image and Vision Computing (publishedVersion)
- OPUS (Augsburg University) (submittedVersion, cc-by-nc-nd)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- affective computing, continuous input, affective state detection, valence prediction, arousal prediction, power prediction, human-computer interaction, human-human interaction, naturalistic behavior
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 ›
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