Abstract
The recognition significance of similar sign language (or confusing gesture) in sign language recognition is highlighted, and the goal is to realize the recognition of such gesture and sign language based on deep learning with an optimized convolutional neural network and the Adam optimizer. The convolutional layer and the pooling layer are connected alternately. The locally connected image data and parameter features are used to extract the shared pooling layer, and the image resolution reduction of image data sampling and the reducibility of iterative training are used to achieve the extraction precision requirements of feature points. In addition, the information transfer between layers is realized through convolution, the introduction of pooling layer and RELU activation function to realize nonlinear mapping and reduce the data dimension. We also use the batch normalization method for faster convergence and dropout method to reduce overfitting. Ten experiments were carried out on a nine-layer “CNN-BN-ReLU-AP-DO” method, with an average accuracy of 97.50 ± 1.65%. The overall accuracy is relatively high, and gesture recognition can be conducted effectively.
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References
Lee, J.S., Lee, Y.J., Lee, E.H..: Hand region extraction and gesture recognition from video stream with complex background through entropy analysis. In: Proceedings of the 26th Annual International Conference of the IEEE EMBS, pp. 1–10 (2004)
Wu, X., Zhang, Q., Xu, Y.: A review of the development of gesture recognition research. Electron. Sci. Technol. 26(6), 71–174 (2013). https://doi.org/10.3969/j.issn.1007-7820.2013.06.053
Jiang, L., Ruan, Q.: Research on gesture recognition technology based on neural network. J. Beijing Jiaotong Univ. 30(6), 32–36 (2006). https://doi.org/10.3969/j.issn.1673-0291.2006.05.008
Feng, Z., Jiang, Y.: A review of gesture recognition research. J. Univ. Jinan (Sci. Technol.) 4, 336–341 (2013)
Gao, Y., Jia, C., Chen, H., Jiang, X.: Chinese fingerspelling sign language recognition using a nine-layer convolutional neural network. EAI Endorsed Trans. e-Learn. 7(20), e2 (2021)
Liu J, Zhao H. School of mechanical and electrical engineering and automation. J. Autom. EI CSCD, 31 (2020)
Zhang, Y., Li, L.: Realization of face feature point recognition based on cascaded convolutional neural network. J. Lanzhou Univ. Technol. 3, 105–109 (2020)
Long, Y., Li, Y., Tao, W., et al.: Text sentiment analysis based on cascade convolution and attention mechanism. J. Taiyuan Normal Univ.: Nat. Sci. 2, 30–36 (2020)
Jiang, X., Chang, L., Zhang, Y.D.: Classification of Alzheimer’s disease via eight-layer convolutional neural network with batch normalization and dropout techniques. J. Med. Imaging Health Inform. 10(5), 1040–1048 (2020)
Xiao, J., Tian, H., Zou, W.: Stereo matching based on convolution neural network. Inform. Technol. Inform. 38(8), 0815017 (2018)
Yuan, M., Zhou, C., Huang, H., et al.: Survey on convolutional neural network pooling methods. Softw. Eng. Appl. 5, 360–372 (2020)
Wang, S.Y., Teng, G.W.: Optimization design of ReLU activation function in convolutional neural network. Inform. Commun. 1673(1131), 42–43 (2018)
M GCMMD: Noisy activation functions. arXiv preprint arXiv 1603:00391 (2016)
Jiang, A., Wang, W.: Research on optimization of ReLU activation function. Transducer Microsyst. Technol. 2, 50–52 (2018)
Liu, J., Zhao, H., Luo, X., Xu, Y.: Research progress of deep learning batch normalization and its related algorithms. Acta Autom. Sinica 46(6), 1090–1120 (2020)
Han, M.: Research and implementation of dropout method based on selective area drop, 003028, https://doi.org/10.27005/d.cnki.gdzku (2020)
Xie, F., Gong, J., Wang, Y.: Facial expression recognition method based on skin color enhancement and block PCA. J. Nanjing Normal Univ. (Eng. Technol. Ed.) 02, 49–56 (2017)
Cai, L., Ye, Y., Gao, X., Li, Z., Zhang, C.: An improved visual SLAM based on affine transformation for ORB feature extraction. Optik 227, 165421 (2021). https://doi.org/10.1016/j.ijleo.2020.165421
Wang, Y., Biyun, X., Kwak, M., Zeng, X.: A noise injection strategy for graph autoencoder training. Neural Comput. Appl. 33(10), 4807–4814 (2020). https://doi.org/10.1007/s00521-020-05283-x
Zhang, X., Zuo, C., Shen, D.: Gamma nonlinear error correction method based on deep learning. G01B11/25, 1–10.
Jiang, X., Satapathy, S.C., Yang, L., Wang, S.-H., Zhang, Y.-D.: A survey on artificial intelligence in Chinese sign language recognition. Arab. J. Sci. Eng. 45(12), 9859–9894 (2020). https://doi.org/10.1007/s13369-020-04758-2
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This work was supported by 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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Gao, Y., Jia, C., Qiao, Y., Huang, X., Lei, J., Jiang, X. (2021). Similar Gesture Recognition via an Optimized Convolutional Neural Network and Adam Optimizer. 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_4
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