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A Study on Emotion Recognition Systems based on the Probabilistic Relational Model Between Facial Expressions and Physiological ResponsesA Study on Emotion Recognition Systems based on the Probabilistic Relational Model Between Facial Expressions and Physiological Responses

Authors
Ko, K.-E.Sim, K.-B.
Issue Date
2013
Publisher
제어·로봇·시스템학회
Keywords
Emotion recognition; Facial expression; Feature fusion; Physiological responses; Probabilistic relational model
Citation
Journal of Institute of Control, Robotics and Systems, v.19, no.6, pp 513 - 519
Pages
7
Journal Title
Journal of Institute of Control, Robotics and Systems
Volume
19
Number
6
Start Page
513
End Page
519
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/19907
DOI
10.5302/J.ICROS.2013.13.1900
ISSN
1976-5622
Abstract
The current vision-based approaches for emotion recognition, such as facial expression analysis, have many technical limitations in real circumstances, and are not suitable for applications that use them solely in practical environments. In this paper, we propose an approach for emotion recognition by combining extrinsic representations and intrinsic activities among the natural responses of humans which are given specific imuli for inducing emotional states. The intrinsic activities can be used to compensate the uncertainty of extrinsic representations of emotional states. This combination is done by using PRMs (Probabilistic Relational Models) which are extent version of bayesian networks and are learned by greedy-search algorithms and expectation-maximization algorithms. Previous research of facial expression-related extrinsic emotion features and physiological signal-based intrinsic emotion features are combined into the attributes of the PRMs in the emotion recognition domain. The maximum likelihood estimation with the given dependency structure and estimated parameter set is used to classify the label of the target emotional states. © ICROS 2013.
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