Facial expression recognition based on Electroencephalogram and facial landmark localization.
Background: Facial expression recognition plays an essential role in affective computing, mental illness diagnosis and rehabilitation. Therefore, facial expression recognition has attracted more and more attention over the years.Objective: The goal of this paper was to improve the accuracy of the El...
| Publicado en: | Technology & Health Care Vol. 27; no. 4; pp. 373 - 388 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Sage Publications Inc.
2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=137796385&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 137796385 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09287329 3QT jtl: Technology & Health Care issn: 09287329 maglogo: N pubinfo: dt: 2019 vid: 27 iid: 4 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 137796385 137796385 NLM30664515 10.3233/THC-181538 NLM30664515 137796385 ppf: 373 ppct: 15 formats: tig: atl: Facial expression recognition based on Electroencephalogram and facial landmark localization. aug: au: Li, Dahua Wang, Zhe Gao, Qiang Song, Yu Yu, Xiao Wang, Chuhan affil: Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems, Tianjin University of Technology, Tianjin 300384, China sug: subj: Electroencephalography Methods Signal Processing, Computer Assisted Mental Disorders Diagnosis Facial Expression Sensitivity and Specificity Algorithms ab: Background: Facial expression recognition plays an essential role in affective computing, mental illness diagnosis and rehabilitation. Therefore, facial expression recognition has attracted more and more attention over the years.Objective: The goal of this paper was to improve the accuracy of the Electroencephalogram (EEG)-based facial expression recognition.Methods: In this paper, we proposed a fusion facial expression recognition method based on EEG and facial landmark localization. The EEG signal processing and facial landmark localization are the two key parts. The raw EEG signals is preprocessed by discrete wavelet transform (DWT). The energy feature vector is composed of energy features of the reconstructed signal. For facial landmark localization, images of the subjects' facial expression are processed by facial landmark localization, and the facial features are calculated by landmarks of essence. In this research, we fused the energy feature vector and facial feature vector, and classified the fusion feature vector with the support vector machine (SVM).Results: From the experiments, we found that the accuracy of facial expression recognition was increased 4.16% by fusion method (86.94 ± 4.35%) than EEG-based facial expression recognition (82.78 ± 5.78%).Conclusion: The proposed method obtain a higher accuracy and a stronger generalization capability. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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