Performance Improvement of Data Fusion Based Real-Time Hand Gesture Recognition by Using 3-D Convolution Neural Networks With Kinect V2

Abstract

Hand gesture recognition is one of the most active areas of research in computer vision. It provides an easy way to interact with a machine without using any extra devices. Hand gestures are natural and intuitive communication way for the human being to interact with his environment. In this paper, we propose Data Fusion Based Real-Time Hand Gesture Recognition using 3-D Convolutional Neural Networks and Kinect V2. To achieve the accurate segmentation and tracking with Kinect V2. Convolution neural network to improve the validity and robustness of the system. Based on the experimental results, the proposed model is accurate, robust and performance with very low processor utilization. The performance of our proposed system in real life application, which is controlling various devices using Kinect V2.