Hand Gesture Recognition from 2D Images by Using Convolutional Capsule Neural Networks

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dc.authoridguler, osman/0000-0003-3272-5973
dc.contributor.authorGuler, Osman
dc.contributor.authorYucedag, Ibrahim
dc.date.accessioned2021-12-01T18:46:56Z
dc.date.available2021-12-01T18:46:56Z
dc.date.issued2021
dc.department[Belirlenecek]en_US
dc.description.abstractObject classification and recognition are an important research area widely used in computer vision and machine learning. With the use of deep learning methods in the field of object recognition, there have been important developments in recent years. Object recognition and its sub-branches face recognition, motion recognition, and hand gesture recognition are now used effectively in devices used in daily life. Hand sign classification and recognition are an area that researchers are working on and trying to develop for human-computer interaction. In this study, a hybrid model was created by using a capsule network algorithm with a convolutional neural network for object classification. A dataset, named HG14, containing 14 different hand gestures was created. To measure the success of the proposed model in object recognition, training was carried out on HG14, FashionMnist, and Cifar-10 datasets. Also, VGG16, ResNet50, DenseNet, and CapsNet models were used to classify the images in HG14, FashionMnist, and Cifar-10 datasets. The results of the training were compared and evaluated. The proposed hybrid model achieved the highest accuracy rates with 90% in the HG14 dataset, 93.88% in the FashionMnist dataset, and 81.42% in the Cifar-10 dataset. The proposed model was found to be successful in all studies compared to other models.en_US
dc.identifier.doi10.1007/s13369-021-05867-2
dc.identifier.issn2193-567X
dc.identifier.issn2191-4281
dc.identifier.scopus2-s2.0-85108787177en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s13369-021-05867-2
dc.identifier.urihttps://hdl.handle.net/20.500.12684/10058
dc.identifier.wosWOS:000666937500011en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofArabian Journal For Science And Engineeringen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectHand gesture recognitionen_US
dc.subjectCNNen_US
dc.subjectCapsule networken_US
dc.subjectHuman-computer interactionen_US
dc.subjectObject classificationen_US
dc.subjectDataseten_US
dc.titleHand Gesture Recognition from 2D Images by Using Convolutional Capsule Neural Networksen_US
dc.typeArticleen_US

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