  Continuous Hand Gesture Recognition (CHGR) has been extensively studied by
researchers in the last few decades. Recently, one model has been presented to
deal with the challenge of the boundary detection of isolated gestures in a
continuous gesture video [17]. To enhance the model performance and also
replace the handcrafted feature extractor in the presented model in [17], we
propose a GCN model and combine it with the stacked Bi-LSTM and Attention
modules to push the temporal information in the video stream. Considering the
breakthroughs of GCN models for skeleton modality, we propose a two-layer GCN
model to empower the 3D hand skeleton features. Finally, the class
probabilities of each isolated gesture are fed to the post-processing module,
borrowed from [17]. Furthermore, we replace the anatomical graph structure with
some non-anatomical graph structures. Due to the lack of a large dataset,
including both the continuous gesture sequences and the corresponding isolated
gestures, three public datasets in Dynamic Hand Gesture Recognition (DHGR),
RKS-PERSIANSIGN, and ASLVID, are used for evaluation. Experimental results show
the superiority of the proposed model in dealing with isolated gesture
boundaries detection in continuous gesture sequences

Computer Vision and Pattern Recognition