A Study on Real-time Hand Pose Recognition and its Classification

Abstract

Hand gesture recognition is a problem. Many computer and human computing have had an interactive community for years, add-on components or existing solutions to users, causing additional computational time requirements. These limitations are mainly human hands due to the camera’s high performance and limited field of view. This work uses a novel method to identify hand-folding metric shape descriptors to generate a 3D cylindrical map of hand-folding with depth information and size and rotation-invariant hand kinematics, and we achieved robust pose recognition in real time. In this paper, new hand force geometry features are proposed for hand pose recognition using the approach. A skeleton arm there was a model built transmits and analyze abduction Finger movements and these variables were added to transform the dimensional probability distribution. A skeleton model was concretized and these parameters were updated to change the dimensional probability distribution. The proposed algorithm very Strong irregular arm division and lateral movement Test results of fingers Show that the proposed method optimal human computing related applications interaction Virtual reality, autonomous driving systems, human machine hand gesture recognition augmented robotics, interfaces and other new, emerging technologies have become an interesting research topic in recent years. Although there are many approaches to a strong authentication System, gesture recognition is based on visual perception, sensors or devices like electronic gloves have many advantages. 10 Visualization to Recognize Hand Positions in an Embedded Computer This article describes a basic recognition implementation. Hand detection is a light curve achieved using a tracking algorithm and neural network. The results show Low power consumption and real-time response accuracy. From this, the proposed system can use to greater extent.