A deep convolutional neural network model for hand gesture recognition in 2D near-infrared images

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dc.authoridKAYA, Yasin/0000-0002-9074-0189
dc.authoridCan, Celal/0000-0002-7631-8934
dc.authoridKilic, Fatih/0000-0002-8550-1562
dc.contributor.authorCan, Celal
dc.contributor.authorKaya, Yasin
dc.contributor.authorKılıç, Fatih
dc.date.accessioned2025-01-06T17:43:16Z
dc.date.available2025-01-06T17:43:16Z
dc.date.issued2021
dc.description.abstractThe hand gesture recognition (HGR) process is one of the most vital components in human-computer interaction systems. Especially, these systems facilitate hearing-impaired people to communicate with society. This study aims to design a deep learning CNN model that can classify hand gestures effectively from the analysis of near-infrared and colored natural images. This paper proposes a new deep learning model based on CNN to recognize hand gestures improving recognition rate, training, and test time. The proposed approach includes data augmentation to boost training. Furthermore, five popular deep learning models are used for transfer learning, namely VGG16, VGG19, ResNet50, DenseNet121, and InceptionV3 and compared their results. These models are applied to recognize 10 different hand gestures for near-infrared images and 24 ASL hand gestures for colored natural images. The proposed CNN model, VGG16, VGG19, Resnet50, DenseNet121, and InceptionV3 models achieve recognition rates of 99.98%, 100%, 99.99%, 91.63%, 82.42% and 81.84%, respectively on near-infrared images. For colored natural ASL images, the models achieve recognition rates of 99.91%, 99.31%, 98.67%, 91.97%, 93.37%, and 93.21%, respectively. The proposed model achieves promising results spending the least amount of time.
dc.identifier.doi10.1088/2057-1976/ac0d91
dc.identifier.issn2057-1976
dc.identifier.issue5
dc.identifier.pmid34157694
dc.identifier.scopus2-s2.0-85111175430
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1088/2057-1976/ac0d91
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2579
dc.identifier.volume7
dc.identifier.wosWOS:000671445200001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherIop Publishing Ltd
dc.relation.ispartofBiomedical Physics & Engineering Express
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjecthuman-computer interaction
dc.subjectgesture recognition
dc.subjectimage analysis
dc.subjectdeep learning
dc.titleA deep convolutional neural network model for hand gesture recognition in 2D near-infrared images
dc.typeArticle

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