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DC poleHodnotaJazyk
dc.contributor.authorBen Salah, Khawla
dc.contributor.authorOthmani, Mohamed
dc.contributor.authorKherallah, Monji
dc.contributor.editorSkala, Václav
dc.date.accessioned2021-09-01T07:33:32Z
dc.date.available2021-09-01T07:33:32Z
dc.date.issued2021
dc.identifier.citationWSCG 2021: full papers proceedings: 29. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 283-292.en
dc.identifier.isbn978-80-86943-34-3
dc.identifier.issn2464-4617
dc.identifier.issn2464–4625(CD/DVD)
dc.identifier.urihttp://hdl.handle.net/11025/45034
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectrPPGcs
dc.subjectodhad srdeční frekvencecs
dc.subjectvideo obličejecs
dc.subjecthluboké učenícs
dc.subjectdiskrétní vlnková transformacecs
dc.subjectdetekce kůžecs
dc.titleContactless Heart Rate Estimation From Facial Video Using Skin Detection and Multi-resolution Analysisen
dc.typeconferenceObjecten
dc.typekonferenční příspěvekcs
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThis paper introduces a remote Photoplethysmography (rPPG), which is used to estimate human heart rate withoutany physical contact, has been extensively applied in multiple fields like medical diagnosis, analysis of humanemotions, rehabilitation training programs, biometric, and fitness assessments. The rPPG signals are usually ex-tracted from facial videos. However, it is still a challenging task due to several contributing factors, e.g., variationin skin tone, lighting condition, and subject’s motion. Accordingly, in this work, a novel approach based on deeplearning skin detection method and the discrete wavelet transform (DWT) is employed to precisely estimate heartrate from facial videos. In the proposed method, by implementing the DWT, the signal is decomposed into approx-imations and details parts thereby it helps in analyzing it at different frequency bands with different resolutions.The results derived from the experiments show that our proposed method outperforms the state-of-the-art methodson the UBFC-RPPG database.en
dc.subject.translatedrPPGen
dc.subject.translatedheart rate estimationen
dc.subject.translatedfacial videoen
dc.subject.translateddeep learningen
dc.subject.translateddiscrete wavelet transformen
dc.subject.translatedskin detectionen
dc.identifier.doihttps://doi.org/10.24132/CSRN.2021.3101.31
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2021: Full Papers Proceedings

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