TY - EJOU AU - Rehman, Muneeb Ur AU - Ahmed, Fawad AU - Khan, Muhammad Attique AU - Tariq, Usman AU - Alfouzan, Faisal Abdulaziz AU - Alzahrani, Nouf M. AU - Ahmad, Jawad TI - Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks T2 - Computers, Materials \& Continua PY - 2022 VL - 70 IS - 3 SN - 1546-2226 AB - 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. KW - Convolutional neural networks; 3D-CNN; LSTM; spatio-temporal; jester; real-time hand gesture recognition DO - 10.32604/cmc.2022.019586