Abstract
Hand gesture identification is one of problems being widely studied. There are two research trends corresponding to two data types, which are static and dynamic gestures. The static gesture is recognized based on the hand shape, while motion is the main feature in identifying dynamic gestures. In this paper, we propose an approach for modeling the dynamic hand gestures based on a combination of two mentioned information. At first, the hand silhouette is extracted using a skin-color filter. A sequence of geometric manipulations is then performed to remove the possible arm. The characteristics which describe the hand shape and motion orientation are estimated. Finally, the k-means clustering technique is combined with hidden Markov model to model each dynamic gesture. The experiments are performed on human-computer interaction dataset and obtain high efficiency.
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