¹School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China Key Laboratory of Gansu Advanced Control for Industrial Process, Lanzhou, China
²School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China
³School of Engineering, Computer Science and Mathematics, University of Exeter，EX4 4QF，United Kingdom

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Abstract—For the whole matching cannot handle partial occlusion and lack of specificity, a new method using Polar- Radius-Invariant-Moment, which is based on Key-Points to extract features for target’s shape recognition, is presented in this paper. Firstly, key-points of the hand shape are extracted through discrete curve evolution method. Secondly, Polar-Radius-Invariant-Moment based on Key- Points is used to describe the characteristics of the gesture shape. Finally, Euclidean distance is utilized in gesture recognition to verify the validity of this method. Hand objects are selected as the test case to testify the performance of the method. Simulation results prove that this method has a better classification character than that of obtained by the Polar-Radius-Invariant-Moment with recognizing the object shape rapidly and accurately, and that it also can keep highly stable even if the object contour was ill-segmented or noisy.

Index Terms—hand shape recognition, key-points, polarradius- invariant-moment, DCE, euclidean distance.

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Cite:Fu Qing Zhao, XinYing Li, QiuYu Zhang, and Jonrinaldi, "Polar-Radius-Invariant-Moment Based on Key- Points for Hand Gesture Shape Recognition," Journal of Software vol. 7, no.1, pp. 88-94, 2012.