Towards a novel biometric facial input for emotion recognition and assistive technology for virtual reality.
Preliminary work using facial electromyography (EMG) to identify facial expressions is reported in this paper. Ten subjects performed 14 different facial expressions following an agreed protocol. Facial EMG signals, measured from surface electrodes were processed and analysed using a machine learnin...
| Publicado en: | International Journal of Child Health & Human Development Vol. 11; no. 2; pp. 243 - 248 |
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| Autores principales: | , , , , , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Nova Science Publishers, Inc.
2018
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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=131463971&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131463971 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 19395965 903J jtl: International Journal of Child Health & Human Development issn: 19395965 maglogo: N pubinfo: dt: 2018 vid: 11 iid: 2 pid: 1040 pub: Nova Science Publishers, Inc. place: Hauppauge, New York artinfo: ui: 131463971 131463971 131463971 131463971 ppf: 243 ppct: 5 formats: fmt: @attributes: type: P tig: atl: Towards a novel biometric facial input for emotion recognition and assistive technology for virtual reality. aug: au: McGhee, James T. Hamedi, Mayhar Fatoorechi, Mohsen Roggen, Daniel Cleal, Andrew Prance, Robert Nduka, Charles affil: Department of Medicine, Imperial College London, South Kensington Campus, London sug: subj: Biometrics Facial Expression Emotions Assistive Technology Virtual Reality Face Perception Human Electromyography Protocols Electrodes Machine Learning Algorithms Validity ab: Preliminary work using facial electromyography (EMG) to identify facial expressions is reported in this paper. Ten subjects performed 14 different facial expressions following an agreed protocol. Facial EMG signals, measured from surface electrodes were processed and analysed using a machine learning algorithm. Our system is able to differentiate facial expressions for assistive input to a high degree of accuracy (99.25%) and posed emotional responses with 100% accuracy. We conclude facial EMG technology has the potential for both assistive input and emotion detection and could replace conventional assistive input devices or video based techniques for use with VR technologies. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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