Abstract
This research introduces an innovative framework for real-time dynamic hand gesture recognition in the field of Human-Computer Interaction (HCI). The framework combines depth learning networks with the integration of multiple datasets to extract both short-term and long-term features from video input. A significant contribution of this research lies in the integration of Convolutional Neural Networks (CNNs) into a specialized short-term memory network (STSNN), enabling the capture of long-term contextual information for accurate gesture recognition. The proposed framework is thoroughly evaluated using two hand-held databases, namely the 14/28 dataset and the LDMI database. By leveraging the computational power of depth learning networks and the fusion of diverse datasets, our model outperforms previous methods, establishing its efficacy in real-time dynamic hand gesture recognition tasks. The outcomes of this research significantly contribute to the advancement of HCI, providing a robust and technically sophisticated solution for gesture-based interfaces. The findings hold promise for enhancing user experiences and facilitating seamless integration of gesture-based interaction techniques across various domains, ultimately improving the efficiency and effectiveness of human-computer interactions.
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Ikram, A., Liu, Y. (2023). Skeleton Based Dynamic Hand Gesture Recognition using Short Term Sampling Neural Networks (STSNN). In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14355. Springer, Cham. https://doi.org/10.1007/978-3-031-46305-1_30
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DOI: https://doi.org/10.1007/978-3-031-46305-1_30
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