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dc.contributor.authorNarayan, S-
dc.contributor.authorMazumdar, A P-
dc.contributor.authorVipparthi, S K-
dc.date.accessioned2024-05-11T10:11:54Z-
dc.date.available2024-05-11T10:11:54Z-
dc.date.issued2024-05-11-
dc.identifier.urihttp://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4449-
dc.description.abstractAbstract: Hand gesture recognition (HGR) plays a significant role in interpreting the meaning of sign language, human–computer interaction, and robot control. This paper proposes a real-time skeleton-based intelligent dynamic hand gesture recognition (SBI-DHGR) approach, which comprises three modules: Palm Centroid (PC), Data augmentation with the Tenet Effect, and Deep learning architecture. The palm Centroid (PC) module is introduced to identify the proposed palm center joint. Data augmentation with a tenet effect module is designed to improve the CNN model’s generalizability. Further, a novel deep learning model is proposed for temporal 3D-HGR by exploiting the capabilities of a multi-channel convolutional neural network (CNN) and long short-term memory (LSTM) recurrent network. The multi-channel CNN is introduced to learn the cardinal positions of hand joints by capturing the low, average, and high-level features. LSTM is embedded to learn the temporal characteristics of hand joints. The effectiveness of the SBI-DHGR framework is evaluated over five challenging datasets: SHREC-14, SHREC-28, DHG-14, DHG-28, and FPHA, by adopting person-dependent and person-independent validation setups.en_US
dc.language.isoen_USen_US
dc.subjectSkeletonen_US
dc.subjectHand-gesture recognitionen_US
dc.subjectMulti-stream CNN architectureen_US
dc.subjectData augmentationen_US
dc.subjectPalm joint contractionen_US
dc.titleSBI-DHGR: Skeleton-based intelligent dynamic hand gestures recognitionen_US
dc.typeArticleen_US
Appears in Collections:Year-2023

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