Performance analysis of static hand gesture recognition approaches using artificial neural network, support vector machine and two stream based transfer learning approach

Gesture recognition is the statistical representation used to identify specific gestures of motion involving the hands, head, face, and/or body. The paper addresses Hand Gesture Recognition (HGR) using novel machine learning and deep learning approaches. Machine learning algorithms viz. Artificial Neural Network (ANN) and Support Vector Machine (SVM) was implemented using spatial features comprising of geometrical features and Fourier descriptors. The experimental results revealed that ANN is better compared with SVM results. A novel decision fusion based two stream transfer learning approach was implemented using deep learning techniques in this paper. The two pretrained models MobileNet (0.5 and 1.0) and Inception V3 were used. The Region Of Interest (ROI) provided by YOLO was used to make a decision of which model is used for testing. The novel approach provided real time 99.6% recognition accuracy. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.