Abstract
With the increasing demand for social security, the performance of expression recognition are getting important for our social life. However, current expression recognition technology has a poor performance in accuracy and speed, especially in the challenge of baby expression recognition. In this paper, we propose a method for baby expression recognition, and design and implement baby expression recognition system bases on a deep learning model. In the system, we build a convolutional neural network model and train a baby expression dataset. During the method, we use a forward propagation neural network, which can loads a picture directly and output the recognition result. The method also provides various functions of baby expression recognition. At last, the experiment shows that our method can balance the requirements of accuracy and speed perfectly.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (61772179), Hunan Provincial Natural Science Foundation of China (2020JJ4152, 2019JJ40005), the Science and Technology Plan Project of Hunan Province(2016TP1020), Scientific Research Fund of Hunan Provincial Education Department (18A333), Double First-Class University Project of Hunan Province (Xiangjiaotong [2018]469), Postgraduate Scientific Research Innovation Project of Hunan Province (CX20190998), Degree & Postgraduate Education Reform Project of Hunan Province (2019JGYB266, 2020JGZD072), Industry University Research Innovation Foundation of Ministry of Education Science and Technology Development Center (2020QT09), Hengyang technology innovation guidance projects (2020jh052805, Hengcaijiaozhi [2020]-67), Postgraduate Teaching Platform Project of Hunan Province (Xiangjiaotong [2019]370–321).
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Zhu, X., Sun, Y., Liu, Q., Xiang, J., Lin, M. (2022). Baby Expression Recognition System Design and Implementation Based on Deep Learning. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_21
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DOI: https://doi.org/10.1007/978-981-16-6554-7_21
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