Abstract Kinect is a promising acquisition device that provides useful information on a scene through color and depth data. There has been a keen interest in utilizing Kinect in many computer vision areas such as gesture recognition. Given the advantages that Kinect provides, hand gesture recognition can be deployed efficiently with minor drawbacks. This paper proposes a simple and yet efficient way of hand gesture recognition via segmenting a hand region from both color and depth data acquired by Kinect v1. The Inception model of the image recognition system is used to check the reliability of the proposed method. Experimental results are derived from a sample dataset of Microsoft Kinect hand acquisitions. Under the appropriate conditions, it is possible to achieve high accuracy in close to real time.

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[29] Vaezi M. and Nekouie M., “3d Human Hand Posture Reconstruction Using A Single 2d Image,” International Journal of Human Computer Interaction, vol. 1, no. 4, pp. 83-94, 2011. Tuktaev Sokhib received the B.S. and M.S. degrees from Tashkent University of Information Technologies, Tashkent, Uzbekistan in 2015 and Gachon University, in the faculty of IT Convergence Engineering, Korea in 2018. His research interests include Computer Vision, Image Processing, Machine/Deep Learning and Artificial Intelligence. Taeg Keun Whangbo received the M.S. degree from City University of New York in 1988 and the Ph.D. degree both in Computer Science from Stevens Institute of Technology in 1995. Currently, he is a professor in the Department of Computer Science, Gachon University, Korea. Before he joined the Gachon University, he was the software developer in Q-Systems which is located in New Jersey from 1988 to 1993. He was also the researcher in Samsung Electronics from 2005 to 2007. From 2006 to 2008, he was the president of the Association of Korea Cultural Technology. His research areas include Computer Graphics, HCI and VR/AR.