International Conference on Computer and Knowledge Engineering

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Dynamic Hand Gesture Recognition with 2DCNN-LSTM and Improved Keyframe Extraction

Authors :

Narjes Heidari¹ Javid Norouzi² Mohammad Sadegh Helfroush³ Habibollah Danyal⁴

1- Shiraz University of Technology 2- Shiraz University of Technology 3- Shiraz University of Technology 4- Shiraz University of Technology

Keywords :

dynamic hand gesture،deep learning،2DCNN،LSTM،keyframe

Abstract :

This paper presents a novel approach to dynamic hand gesture recognition that surpasses existing methods in accuracy and efficiency. By combining deep learning and clustering techniques, we propose a new framework for keyframe extraction that effectively captures representative video frames. Furthermore, a novel data augmentation method is introduced to enhance the robustness of our proposed 2DCNN-LSTM model. Our model achieves a state-of-the-art accuracy of 98.69% on the Northwestern Hand gesture Dataset, demonstrating the effectiveness of our approach in recognizing complex hand gestures. This research contributes to advancing the field of human-computer interaction by providing a robust and accurate method for dynamic hand gesture recognition, with potential applications in virtual reality, augmented reality, and human-robot interaction.

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