A realtime hand gesture recognition based on Haar wavelet representation

Nowadays, sign language is commonly used as a communication language for auditory handicapped people. In addition to voice and controller pads, hand gestures can also be an effective way of communication between humans and robots or even between auditory handicapped people and robots. To be an effective sign recognition system, it should be glove-free, fast, small database and accurate. In this project, we aim to propose a hand gesture recognition system which performs realtime recognition. We reduce the database size by standardizing the orientation of hands using the idea of principal axis. To facilitate the matching process, we propose a codeword scheme based on the features of hand gestures. We also propose the use of a new measurement metric with a penalty score which enhances the recognition accuracy. Experimental results have proved the success of the proposed hand gesture recognition system in terms of time, database size, and accuracy.