Abstract—In this paper, we propose a novel gesture recognition system using depth data captured by Kinect sensor. Conventionally, the features which have been used for hand gesture recognition are divided into two parts, hand shape features and arm movement features. However, these traditional features are not robust for environmental changing such as individual differences in body size, camera position and so on. In this paper, we propose a novel hand gesture recognition system using depth data, which is robust for environmental changing. Our approach involves an extraction of hand shape features based on gradient value instead of conventional 2D shape features, and arm movement features based on angles between each joints. In order to show the effectiveness of the proposed method, a performance is evaluated comparing with the conventional method by using Japanese sign language.
Index Terms—Image processing, hand gesture recognition, depth sensor, HMM.
Hironori Takimoto and Akihiro Kanagawa are with Okayama Prefectural University, 111, Kuboki, Soja, Okayama, 719-1197, Japan (e-mail: takimoto@c.oka-pu.ac.jp; kanagawa@c.oka-pu.ac.jp).
Lee Jaemin is with SuperSoftware Co., Ltd., 1-7-13, Ebisu, Shibuya-ku, Tokyo, 150-0013, Japan (e-mail: zaemin2@gmail.com).
Cite:Hironori Takimoto, Jaemin Lee, and Akihiro Kanagawa, "A Robust Gesture Recognition Using Depth Data," International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 245-249, 2013.