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  4. Publikationen School of Engineering
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dc.contributor.authorDürr, Oliver-
dc.contributor.authorPauchard, Yves-
dc.contributor.authorBrowarnik, Diego Hernan-
dc.contributor.authorAxthelm, Rebekka-
dc.contributor.authorLoeser, Martin-
dc.date.accessioned2018-12-17T08:16:34Z-
dc.date.available2018-12-17T08:16:34Z-
dc.date.issued2015-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/13891-
dc.description.abstractIn this paper we describe a fast and accurate pipeline for real-time face recognition that is based on a convolutional neural network (CNN) and requires only moderate computational resources. After training the CNN on a desktop PC we employed a Raspberry Pi, model B, for the classification procedure. Here, we reached a performance of approximately 2 frames per second and more than 97% recognition accuracy. The proposed approach outperforms all of OpenCV's algorithms with respect to both accuracy and speed and shows the applicability of recent deep learning techniques to hardware with limited computational performance.de_CH
dc.language.isoende_CH
dc.publisherThe Eurographics Associationde_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectFace recognitionde_CH
dc.subjectRaspberry Pide_CH
dc.subjectDeep learningde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.titleDeep learning on a Raspberry Pi for real time face recognitionde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementSchool of Engineeringde_CH
zhaw.organisationalunitInstitut für Informatik (InIT)de_CH
zhaw.organisationalunitInstitut für Datenanalyse und Prozessdesign (IDP)de_CH
zhaw.organisationalunitInstitute of Signal Processing and Wireless Communications (ISC)de_CH
dc.identifier.doi10.2312/egp.20151036de_CH
zhaw.conference.detailsEurographics Conference (EG 2015), Zurich, 4-8 May 2015de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.pages.end12de_CH
zhaw.pages.start11de_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.title.proceedingsEG 2015 - Postersde_CH
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Dürr, O., Pauchard, Y., Browarnik, D. H., Axthelm, R., & Loeser, M. (2015). Deep learning on a Raspberry Pi for real time face recognition [Conference poster]. EG 2015 - Posters, 11–12. https://doi.org/10.2312/egp.20151036
Dürr, O. et al. (2015) ‘Deep learning on a Raspberry Pi for real time face recognition’, in EG 2015 - Posters. The Eurographics Association, pp. 11–12. Available at: https://doi.org/10.2312/egp.20151036.
O. Dürr, Y. Pauchard, D. H. Browarnik, R. Axthelm, and M. Loeser, “Deep learning on a Raspberry Pi for real time face recognition,” in EG 2015 - Posters, 2015, pp. 11–12. doi: 10.2312/egp.20151036.
DÜRR, Oliver, Yves PAUCHARD, Diego Hernan BROWARNIK, Rebekka AXTHELM und Martin LOESER, 2015. Deep learning on a Raspberry Pi for real time face recognition. In: EG 2015 - Posters. Conference poster. The Eurographics Association. 2015. S. 11–12
Dürr, Oliver, Yves Pauchard, Diego Hernan Browarnik, Rebekka Axthelm, and Martin Loeser. 2015. “Deep Learning on a Raspberry Pi for Real Time Face Recognition.” Conference poster. In EG 2015 - Posters, 11–12. The Eurographics Association. https://doi.org/10.2312/egp.20151036.
Dürr, Oliver, et al. “Deep Learning on a Raspberry Pi for Real Time Face Recognition.” EG 2015 - Posters, The Eurographics Association, 2015, pp. 11–12, https://doi.org/10.2312/egp.20151036.


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