American sign language recognition using HAAR type classifier

Abstract

In this paper, we introduce a hand gesture recognition system to recognize real time gesture in unconstrained environment. We consider fixed set of manual commands, and develop a simple, yet affective procedure for gesture recognition. The system consists of three modules: real time hand tracking, gesture set training and gesture recognition. We have used Viola Jones Object detection framework which employs a variant of the learning algorithm AdaBoost to both select the best features and to train classifiers that use them. We have tested our system vocabulary of four gestures and results effectiveness of this approach using principal component analysis and Eigen vectors.