A Study on Emotion Recognition Systems based on the Probabilistic Relational Model Between Facial Expressions and Physiological Responses
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ko, K.-E. | - |
dc.contributor.author | Sim, K.-B. | - |
dc.date.available | 2019-05-29T03:39:55Z | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 1976-5622 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/19907 | - |
dc.description.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. | - |
dc.format.extent | 7 | - |
dc.language | 한국어 | - |
dc.language.iso | KOR | - |
dc.publisher | 제어·로봇·시스템학회 | - |
dc.title | A Study on Emotion Recognition Systems based on the Probabilistic Relational Model Between Facial Expressions and Physiological Responses | - |
dc.title.alternative | A Study on Emotion Recognition Systems based on the Probabilistic Relational Model Between Facial Expressions and Physiological Responses | - |
dc.type | Article | - |
dc.identifier.doi | 10.5302/J.ICROS.2013.13.1900 | - |
dc.identifier.bibliographicCitation | Journal of Institute of Control, Robotics and Systems, v.19, no.6, pp 513 - 519 | - |
dc.identifier.kciid | ART001773377 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-84887167496 | - |
dc.citation.endPage | 519 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 513 | - |
dc.citation.title | Journal of Institute of Control, Robotics and Systems | - |
dc.citation.volume | 19 | - |
dc.type.docType | Article | - |
dc.publisher.location | 대한민국 | - |
dc.subject.keywordAuthor | Emotion recognition | - |
dc.subject.keywordAuthor | Facial expression | - |
dc.subject.keywordAuthor | Feature fusion | - |
dc.subject.keywordAuthor | Physiological responses | - |
dc.subject.keywordAuthor | Probabilistic relational model | - |
dc.subject.keywordPlus | Emotion recognition | - |
dc.subject.keywordPlus | Facial Expressions | - |
dc.subject.keywordPlus | Feature fusion | - |
dc.subject.keywordPlus | Physiological response | - |
dc.subject.keywordPlus | Probabilistic relational models | - |
dc.subject.keywordPlus | Algorithms | - |
dc.subject.keywordPlus | Bayesian networks | - |
dc.subject.keywordPlus | Maximum likelihood estimation | - |
dc.subject.keywordPlus | Physiology | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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