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DC Field | Value | Language |
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dc.contributor.author | Narayan, S | - |
dc.contributor.author | Mazumdar, A P | - |
dc.contributor.author | Vipparthi, S K | - |
dc.date.accessioned | 2024-05-11T10:11:54Z | - |
dc.date.available | 2024-05-11T10:11:54Z | - |
dc.date.issued | 2024-05-11 | - |
dc.identifier.uri | http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4449 | - |
dc.description.abstract | Abstract: 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.iso | en_US | en_US |
dc.subject | Skeleton | en_US |
dc.subject | Hand-gesture recognition | en_US |
dc.subject | Multi-stream CNN architecture | en_US |
dc.subject | Data augmentation | en_US |
dc.subject | Palm joint contraction | en_US |
dc.title | SBI-DHGR: Skeleton-based intelligent dynamic hand gestures recognition | en_US |
dc.type | Article | en_US |
Appears in Collections: | Year-2023 |
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Full Text.pdf | 2.48 MB | Adobe PDF | View/Open Request a copy |
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