Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.13091/5596
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Akdag, Ali | - |
dc.contributor.author | Baykan, Ömer Kaan | - |
dc.date.accessioned | 2024-06-01T08:58:11Z | - |
dc.date.available | 2024-06-01T08:58:11Z | - |
dc.date.issued | 2024 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | https://doi.org/10.3390/electronics13081591 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.13091/5596 | - |
dc.description.abstract | This study introduces an innovative multichannel approach that focuses on the features and configurations of fingers in isolated sign language recognition. The foundation of this approach is based on three different types of data, derived from finger pose data obtained using MediaPipe and processed in separate channels. Using these multichannel data, we trained the proposed MultiChannel-MobileNetV2 model to provide a detailed analysis of finger movements. In our study, we first subject the features extracted from all trained models to dimensionality reduction using Principal Component Analysis. Subsequently, we combine these processed features for classification using a Support Vector Machine. Furthermore, our proposed method includes processing body and facial information using MobileNetV2. Our final proposed sign language recognition method has achieved remarkable accuracy rates of 97.15%, 95.13%, 99.78%, and 95.37% on the BosphorusSign22k-general, BosphorusSign22k, LSA64, and GSL datasets, respectively. These results underscore the generalizability and adaptability of the proposed method, proving its competitive edge over existing studies in the literature. | en_US |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.relation.ispartof | Electronics | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | sign language recognition | en_US |
dc.subject | deep learning | en_US |
dc.subject | feature fusion | en_US |
dc.title | Multi-Stream Isolated Sign Language Recognition Based on Finger Features Derived from Pose Data | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.3390/electronics13081591 | - |
dc.identifier.scopus | 2-s2.0-85191396116 | en_US |
dc.department | KTÜN | en_US |
dc.identifier.volume | 13 | en_US |
dc.identifier.issue | 8 | en_US |
dc.identifier.wos | WOS:001210168900001 | en_US |
dc.institutionauthor | Baykan, Ömer Kaan | - |
dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
dc.authorscopusid | 57200269812 | - |
dc.authorscopusid | 23090480800 | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
item.openairetype | Article | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
item.cerifentitytype | Publications | - |
crisitem.author.dept | 02.03. Department of Computer Engineering | - |
Appears in Collections: | Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections |
CORE Recommender
Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.