Cited by 360Emotion and Mood RecognitionSentiment Analysis and Opinion MiningContext-Aware Activity Recognition Systems

Categorical and dimensional affect analysis in continuous input: Current trends and future directions

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Gunes, Hatice, Schuller, Björn·2013·Image and Vision Computing

Abstract

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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Crossref · 10.1016/j.imavis.2012.06.016

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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].”

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Authors
Gunes, H., & Schuller, B.
Affiliations
Technical University of Munich (DE)
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
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
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

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