Posture labeling based gesture classification for Turkish sign language using depth values

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dc.contributor.authorSaykol, Ediz
dc.contributor.authorTure, Halit Talha
dc.contributor.authorSirvanci, Ahmet Mert
dc.contributor.authorTuran, Mert
dc.date.accessioned2024-03-13T10:35:39Z
dc.date.available2024-03-13T10:35:39Z
dc.date.issued2016
dc.departmentİstanbul Beykent Üniversitesien_US
dc.description.abstractPurpose - The purpose of this paper to classify a set of Turkish sign language (TSL) gestures by posture labeling based finite-state automata (FSA) that utilize depth values in location-based features. Gesture classification/recognition is crucial not only in communicating visually impaired people but also for educational purposes. The paper also demonstrates the practical use of the techniques for TSL. Design/methodology/approach - Gesture classification is based on the sequence of posture labels that are assigned by location-based features, which are invariant under rotation and scale. Grid-based signing space clustering scheme is proposed to guide the feature extraction step. Gestures are then recognized by FSA that process temporally ordered posture labels. Findings - Gesture classification accuracies and posture labeling performance are compared to k-nearest neighbor to show that the technique provides a reasonable framework for recognition of TSL gestures. A challenging set of gestures is tested, however the technique is extendible, and extending the training set will increase the performance. Practical implications - The outcomes can be utilized as a system for educational purposes especially for visually impaired children. Besides, a communication system would be designed based on this framework. Originality/value - The posture labeling scheme, which is inspired from keyframe labeling concept of video processing, is the original part of the proposed gesture classification framework. The search space is reduced to single dimension instead of 3D signing space, which also facilitates design of recognition schemes. Grid-based clustering scheme and location-based features are also new and depth values are received from Kinect. The paper is of interest for researchers in pattern recognition and computer vision.en_US
dc.identifier.doi10.1108/K-04-2015-0107
dc.identifier.endpage621en_US
dc.identifier.issn0368-492X
dc.identifier.issn1758-7883
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-84963998015en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage604en_US
dc.identifier.urihttps://doi.org/10.1108/K-04-2015-0107
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4529
dc.identifier.volume45en_US
dc.identifier.wosWOS:000376199300004en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherEmerald Group Publishing Ltden_US
dc.relation.ispartofKybernetesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectImage processingen_US
dc.subjectAutomata theoryen_US
dc.subjectKinect sensoren_US
dc.titlePosture labeling based gesture classification for Turkish sign language using depth valuesen_US
dc.typeArticleen_US

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