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This paper proposes a method to recognize bare hand gestures using a dynamic vision sensor (DVS) camera. Different from conventional cameras, DVS cameras only respond to pixels with temporal luminance differences, which can greatly reduce the computational cost of comparing consecutive frames to track moving objects. Due to differences in available information, conventional vision techniques for gesture recognition may not be directly applicable in DVS based applications. This paper attempts to classify three different hand gestures made by a player during rock-paper-scissors game. We propose novel methods to detect the point where the player delivers a throw, to extract hand regions, and to extract useful features for machine learning based classification. Preliminary results show that our method produces enhanced accuracy of hand gesture recognition.