Abstract
A novel hand gesture recognition algorithm is proposed for human-computer interaction, which is based on RGB-D image (RGB image and Depth image) and arm removal. The hand is firstly extracted from the background based on depth data and skin-color features. Then the arm area is removed by using distance transformation operations, and gesture composed of palm and fingers is obtained. Finally Hu moments of the gesture are calculated and entered into Support Vector Machine (SVM) for recognition. Experimental results demonstrate that the proposed algorithm can recognize 8 gestures with an accuracy of 95.83% in the complex background.
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Acknowledgements
The work is supported by National Natural Science Foundation of China (61372142, U1401252, U1404603), Guangdong Province Science and technology plan (2013B010102004, 2013A011403003), Guangzhou city science and technology research projects (201508010023).
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Xu, B., Zhou, Z., Huang, J., Huang, Y. (2017). Static Hand Gesture Recognition Based on RGB-D Image and Arm Removal. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_22
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DOI: https://doi.org/10.1007/978-3-319-59072-1_22
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