REAL TIME HAND GESTURE RECOGNITION SYSTEM

Abstract

Hand gestures can be used for natural and intuitive human-computer interaction. Our new method combines existing techniques of skin color based ROI segmentation and Viola-Jones Haar-like feature based object detection, to optimize hand gesture recognition for mouse operation. A mouse operation has two parts, movement of cursor and clicking using the right or left mouse button. In this paper, color is used as a robust feature to first define a Region of Interest (ROI). Then within this ROI, hand postures are detected by using Haar-like features and AdaBoost learning algorithm. The AdaBoost learning algorithm significantly speeds up the performance and constructs an accurate cascaded classifier by combining a sequence of weak classifiers.