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An Optimized Seven-Layer Convolutional Neural Network with Data Augmentation for Classification of Chinese Fingerspelling Sign Language

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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST,volume 388)

Abstract

Sign language recognition especially finger language recognition facilitates the life of deaf people in China. It overcomes many difficulties and provides convenience for deaf people’s life. In this paper, we used the advanced convolutional neural network to extract the different characteristics of the input. We created an optimized seven-layer CNN, including five convolution layers for feature extraction and two fully connected layers for classification to enhance the original signal function and reduce noise after operation. Some advanced techniques such as batch normalization, ReLu and dropout were employed to optimize the neural network. Meanwhile, we adopted data augmentation technology, which not only expanded the data set and improve the performance of machine learning algorithm, but also avoided the over-fitting problem. The experimental results show that the average recognition accuracy reaches 91.99 ± 1.21%, which indicate an excellent property.

Keywords

  • Convolutional neural network
  • Data augmentation
  • Chinese fingerspelling sing language
  • Batch normalization
  • ReLU
  • Maximum pooling
  • Dropout

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Acknowledgements

This work was supported by National Philosophy and Social Sciences Foundation (20BTQ065), 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).

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Correspondence to Xianwei Jiang .

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Gao, Y., Zhu, R., Gao, R., Weng, Y., Jiang, X. (2021). An Optimized Seven-Layer Convolutional Neural Network with Data Augmentation for Classification of Chinese Fingerspelling Sign Language. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-030-82565-2_3

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  • DOI: https://doi.org/10.1007/978-3-030-82565-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82564-5

  • Online ISBN: 978-3-030-82565-2

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