Abstract
Sign language plays a significant role in smooth communication between the hearing-impaired and the healthy. Chinese fingerspelling is an important composition of Chinese sign language, which is suitable for denoting terminology and using as basis of gesture sign language learning. We proposed a Chinese fingerspelling recognition approach via Hu moment invariant and RBF support vector machine. Hu moment invariant was employed to extract image feature and RBF-SVM was employed to classify. Meanwhile, 10-fold across validation was introduced to avoid overfitting. Our method HMI-RBF-SVM achieved overall accuracy of 86.47 ± 1.15% and was superior to three state-of-the-art approaches.
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Acknowledgement
This work was supported from Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-aged Teachers and Presidents of China, The Natural Science Foundation of Jiangsu Higher Education Institutions of China (19KJA310002), The Philosophy and Social Science Research Foundation Project of Universities of Jiangsu Province (2017SJB0668), The Natural Science Foundation of Jiangsu Province (16KJB520029).
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Gao, Y. et al. (2020). Chinese Fingerspelling Recognition via Hu Moment Invariant and RBF Support Vector Machine. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_34
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DOI: https://doi.org/10.1007/978-3-030-51103-6_34
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