Affiliations: [a] Xinjiang Institute of Technology, Aksu, Xinjiang 843100, China | [b] College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China
Correspondence:
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Corresponding author: Hailong Huang, College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou, Zhejiang 325035, China. E-mail: 172292001@qq.comorhailonghuang246@126.com.
Abstract: Aiming at the problem that hand gesture recognition difficult issues in the real scene, a method of hand gesture recognition that combines skin color with SVM was proposed. The skin color area was separated by Otsu adaptive threshold algorithm in the YCbCr color space, and hand gesture was segmented by hand gesture area criterion. Hu moment features and finger number were extracted on the hand gesture contour as the feature vector. Six common static gestures were classified and recognized by SVM classifier. Experimental result showed that this method had good stability and real-time performance, average recognition rate could reach 94%. On the hand gesture recognition application, the hand gesture recognition results were converted into instructions, which achieved the real-time controlled simulation of the NAO robot in Webots simulation environment, and verified the feasibility of hand gesture recognition algorithm.
Keywords: Hand gesture recognition, color segmentation, Hu moments, fingertip detection, SVM classifier, Webots
