@Article{cmc.2022.019586,
AUTHOR = {Muneeb Ur Rehman, Fawad Ahmed, Muhammad Attique Khan, Usman Tariq, Faisal Abdulaziz Alfouzan, Nouf M. Alzahrani, Jawad Ahmad},
TITLE = {Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {70},
YEAR = {2022},
NUMBER = {3},
PAGES = {4675--4690},
URL = {http://www.techscience.com/cmc/v70n3/44942},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2022.019586}
}