Gesture Recognition using CNN and RNN
Rajalakshmi J1, Kumar P2
1Rajalakshmi J, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
2Dr. P. Kumar, Professor, Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, India.
Manuscript received on July 05, 2020. | Revised Manuscript received on July 12, 2020. | Manuscript published on July 30, 2020. | PP: 230-233 | Volume-9 Issue-2, July 2020. | Retrieval Number: B3417079220/2020©BEIESP | DOI: 10.35940/ijrte.B3417.079220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Gesture Recognition is a major area in Human-Computer Interaction (HCI). HCI allows computers to capture and interpret human gestures as commands. A real-time Hand Gesture Recognition System is implemented and is used for operating electronic appliances. This system is implemented using the deep learning models such as the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN). The combined model will effectively recognize both static and dynamic hand gestures. Also the model accuracy while using VGG16 pre-trained CNN model is investigated.
Keywords: Convolution Neural Network (CNN), Human-Computer Interaction (HCI), Recurrent Neural Network (RNN).