AN EFFICIENT METHOD FOR HAND GESTURE RECOGNITION USING ROBUST FEATURES VECTOR

Abstract

Integration of technology for the Fourth Industrial Revolution (IR 4.0) has increased the need for efficient methods for developing dynamic human computer interfaces and virtual environments. In this context, hand gesture recognition can play a vital role to serve as a natural mode of interactive human machine interaction. Unfixed brightness, complex backgrounds, color constraints, dependency on hand shape, rotation, and scale variance are the challenging issues which have an impact on robust performance for the existing methods as per outlined in previous researches. This research presents an efficient method for hand gesture recognition by constructing a robust features vector. The proposed method is performed in two phases, where in the first phase the features vector is constructed by selecting interest points at distinctive locations using a blob detector based on Hessian matrix approximation. After detecting the area of the hand from the features vector, edge detection is applied in the isolated hand followed by edge orientation computation. After this, templates are generated using one and two dimensional mapping to compare candidate and prototype images using adaptive threshold. The proposed research performed extensive experimentation, where a recognition accuracy rate of 98.33% was achieved by it, which is higher as compared to previous research results. Experimental results reveal the effectiveness of the proposed methodology in real time.