This study presents a novel algorithm to recognize a set of static hand gestures for the Human-Computer Interaction (HCI), based on hand segmentation using both wavelet network for images feature extraction , and supervised feed-forward neural network with back propagation training algorithm for recognition. One hundred and twenty hand gesture images were used for training and 60 for testing. The best classification rate of 97% was obtained for the testing set.
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