Research Article
Chinese Fingerspelling Recognition via Hu Moment Invariant and RBF Support Vector Machine
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@INPROCEEDINGS{10.1007/978-3-030-51103-6_34, author={Ya Gao and Ran Wang and Chen Xue and Yalan Gao and Yifei Qiao and Chengchong Jia and Xianwei Jiang}, title={Chinese Fingerspelling Recognition via Hu Moment Invariant and RBF Support Vector Machine}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2020}, month={7}, keywords={Hu moment invariant RBF Support Vector Machine Chinese fingerspelling recognition}, doi={10.1007/978-3-030-51103-6_34} }- Ya Gao
Ran Wang
Chen Xue
Yalan Gao
Yifei Qiao
Chengchong Jia
Xianwei Jiang
Year: 2020
Chinese Fingerspelling Recognition via Hu Moment Invariant and RBF Support Vector Machine
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-51103-6_34
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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