Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks |
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Authors: | Muneeb Ur Rehman Fawad Ahmed Muhammad Attique Khan Usman Tariq Faisal Abdulaziz Alfouzan Nouf M. Alzahrani Jawad Ahmad |
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Abstract: | Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out. The proposed model is a light-weight architecture with only 3.7 million training parameters. The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly. The model was trained on 2000 video-clips per class which were separated into 80% training and 20% validation sets. An accuracy of 99% and 97% was achieved on training and testing data, respectively. We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2 + LSTM. |
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Keywords: | Convolutional neural networks 3D-CNN LSTM spatio-temporal jester real-time hand gesture recognition |
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