Abstract
In this paper, we introduce a hand gesture recognition system to recognize real-time gesture in unconstrained environments. The system consists of three modules: real-time hand tracking, training gesture and gesture recognition using pseudo two dimensions hidden Markov models (P2-DHMMs). We have used a Kalman filter and hand blobs analysis for hand tracking to obtain motion descriptors and hand region. It is fairly robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, there have been proposed to improve the overall performance of the approach: (1) Intelligent selection of training images and (2) Adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. A gesture recognition system which can reliably recognize single-hand gestures in real time on standard hardware is developed. In the experiments, we have tested our system to the vocabulary of 36 gestures including the America sign language (ASL) letter spelling alphabet and digits and results in the effectiveness of the approach.
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