Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.11779/1302
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DC Field | Value | Language |
---|---|---|
dc.contributor.author | Aktaş, Müjde | - |
dc.contributor.author | Gökberk, Berk | - |
dc.contributor.author | Akarun, Lale | - |
dc.date.accessioned | 2020-02-07T14:59:58Z | |
dc.date.available | 2020-02-07T14:59:58Z | |
dc.date.issued | 2019 | - |
dc.identifier.citation | Aktaş, M., Gökberk, B. & Akarun, L., (6-9 November, 2019). Recognizing non-manual signs in Turkish sign language, 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA 2019), pp 1-6, DOI: https://doi.org/10.1109/IPTA.2019.8936081 | en_US |
dc.identifier.isbn | 9781728139753 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.11779/1302 | - |
dc.identifier.uri | https://doi.org/10.1109/IPTA.2019.8936081 | - |
dc.description.abstract | Recognition of non-manual components in sign language has been a neglected topic, partly due to the absence of annotated non-manual sign datasets. We have collected a dataset of videos with non-manual signs, displaying facial expressions and head movements and prepared frame-level annotations. In this paper, we present the Turkish Sign Language (TSL) non-manual signs dataset and provide a baseline system for non-manual sign recognition. A deep learning based recognition system is proposed, in which the pre-trained ResNet Convolutional Neural Network (CNN) is employed to recognize question, negation side to side and negation up-down, affirmation and pain movements and expressions. Our subject independent method achieves 78.49% overall frame-level accuracy on 483 TSL videos performed by six subjects, who are native TSL signers. Prediction results of consecutive frames are filtered for analyzing the qualitative results. | en_US |
dc.description.sponsorship | IEEE France Section, IEEE Turkey Section, Universite Paris-Saclay, Yeditepe University. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.relation.ispartof | 9th International Conference on Image Processing Theory, Tools and Applications, IPTA 2019 | en_US |
dc.rights | info:eu-repo/semantics/closedAccess | en_US |
dc.subject | Sign Language Recognition | en_US |
dc.subject | Non-Manual Sign Analysis | en_US |
dc.subject | Facial Expression Recognition | en_US |
dc.title | Recognizing non-manual signs in Turkish sign language | en_US |
dc.type | Conference Object | en_US |
dc.identifier.doi | 10.1109/IPTA.2019.8936081 | - |
dc.identifier.scopus | 2-s2.0-85077967469 | en_US |
dc.authorid | Berk Gökberk / 0000-0001-6299-1610 | - |
dc.description.woscitationindex | Conference Proceedings Citation Index - Science | - |
dc.description.WoSDocumentType | Proceedings Paper | |
dc.description.WoSPublishedMonth | Kasım | en_US |
dc.description.WoSIndexDate | 2019 | en_US |
dc.description.WoSYOKperiod | YÖK - 2019-20 | en_US |
dc.relation.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
dc.identifier.endpage | 6 | en_US |
dc.identifier.startpage | 1 | en_US |
dc.department | Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü | en_US |
dc.identifier.wos | WOS:000529320000011 | en_US |
dc.institutionauthor | Gökberk, Berk | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.grantfulltext | embargo_20400130 | - |
item.languageiso639-1 | en | - |
item.cerifentitytype | Publications | - |
item.fulltext | With Fulltext | - |
item.openairetype | Conference Object | - |
Appears in Collections: | Bilgisayar Mühendisliği Bölümü koleksiyonu Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
Files in This Item:
File | Description | Size | Format | |
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BerkGökberk.pdf Until 2040-01-30 | Yayıncı Sürümü - Konferans Dosyası | 1.58 MB | Adobe PDF | View/Open Request a copy |
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