Physiological responses-based emotion recognition using multi-class SVM with RBF Kernel
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Vanny, M. | - |
dc.contributor.author | Ko, K.-E. | - |
dc.contributor.author | Park, S.-M. | - |
dc.contributor.author | Sim, K.-B. | - |
dc.date.available | 2019-05-29T03:39:53Z | - |
dc.date.issued | 2013 | - |
dc.identifier.issn | 1976-5622 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/19906 | - |
dc.description.abstract | Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions. © ICROS 2013. | - |
dc.format.extent | 8 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | 제어·로봇·시스템학회 | - |
dc.title | Physiological responses-based emotion recognition using multi-class SVM with RBF Kernel | - |
dc.title.alternative | Physiological Responses-Based Emotion Recognition Using Multi-Class SVM with RBF Kernel | - |
dc.type | Article | - |
dc.identifier.doi | 10.5302/J.ICROS.2013.13.1879 | - |
dc.identifier.bibliographicCitation | Journal of Institute of Control, Robotics and Systems, v.19, no.4, pp 364 - 371 | - |
dc.identifier.kciid | ART001757251 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-84887168510 | - |
dc.citation.endPage | 371 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 364 | - |
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 | Biofeedback system | - |
dc.subject.keywordAuthor | Emotion recognition | - |
dc.subject.keywordAuthor | Multi-class SVM Gaussian RBF | - |
dc.subject.keywordAuthor | Physiological signals | - |
dc.subject.keywordAuthor | Visual-stimuli | - |
dc.subject.keywordPlus | Emotion recognition | - |
dc.subject.keywordPlus | Emotional state | - |
dc.subject.keywordPlus | Gaussians | - |
dc.subject.keywordPlus | Multiclass SVM | - |
dc.subject.keywordPlus | Physiological signals | - |
dc.subject.keywordPlus | Radial basis functions | - |
dc.subject.keywordPlus | Standard deviation | - |
dc.subject.keywordPlus | Visual-stimuli | - |
dc.subject.keywordPlus | Feature extraction | - |
dc.subject.keywordPlus | Gaussian distribution | - |
dc.subject.keywordPlus | Human computer interaction | - |
dc.subject.keywordPlus | Physiology | - |
dc.subject.keywordPlus | Radial basis function networks | - |
dc.subject.keywordPlus | Support vector machines | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
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