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

被引:233
作者
Gunes, Hatice [1 ]
Schuller, Bjoern [2 ]
机构
[1] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London, England
[2] Tech Univ Munich, Inst Human Machine Commun, D-80290 Munich, Germany
关键词
Automatic affect analysis; Continuous input; Multiple modalities; Categorical affect description; Dimensional affect description; Survey; PLEASURE-AROUSAL-DOMINANCE; EMOTION RECOGNITION; FACIAL EXPRESSION; POSTURES; FRAMEWORK; NETWORKS; BEHAVIOR; VALENCE; MOTION; MEMORY;
D O I
10.1016/j.imavis.2012.06.016
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (C) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码:120 / 136
页数:17
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