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A Novel Action Transformer Network for Hybrid Multimodal Sign Language Recognition

Computers, Materials & Continua 2023年第1期74卷 523-537页

作者： sameena javaid Safdar Rizvi Department of Computer Sciences School of Engineering and Applied SciencesBahria UniversityKarachi CampusKarachiPakistan

Sign language fills the communication gap for people with hearing and speaking *** includes both visual modalities,manual gestures consisting of movements of hands,and non-manual gestures incorporating body movements including head,facial expressions,eyes,shoulder shrugging,*** both gestures have been detected;identifying separately may have better accuracy,butmuch communicational information is *** sign language mechanism is needed to detect manual and non-manual gestures to convey the appropriate detailed message to *** novel proposed system contributes as Sign LanguageAction Transformer Network(SLATN),localizing hand,body,and facial gestures in video *** we are expending a Transformer-style structural design as a“base network”to extract features from a spatiotemporal *** impulsively learns to track individual persons and their action context inmultiple ***,a“head network”emphasizes hand movement and facial expression simultaneously,which is often crucial to understanding sign language,using its attention mechanism for creating tight bounding boxes around classified *** model’s work is later compared with the traditional identification methods of activity *** not only works faster but achieves better accuracy as *** achieves overall 82.66%testing accuracy with a very considerable performance of computation with 94.13 Giga-Floating Point Operations per Second(G-FLOPS).Another contribution is a newly created dataset of Pakistan Sign Language forManual and Non-Manual(PkSLMNM)gestures.

关键词： Sign language gesture recognition manual signs non-manual signs action transformer network

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