Abstract
The recognition rate of traditional face recognition algorithm to the face image with occlusion is not high, resulting in poor recognition effect. Therefore, this paper proposes a partial occlusion face recognition algorithm based on recurrent neural network. According to the different light sources, the high filtering function is used to analyze the halo effect of the image, realize the preprocessing of partially occluded face image, set up the global face feature area and the local face feature area according to the image features, and extract the global and local features of the image; based on the time and structure features of the recursive neural network, establish the local subspace, and realize the local face image recognition Law. The experimental results show that: compared with the traditional algorithm, the face recognition algorithm studied in this paper has a higher recognition rate, and can accurately recognize the partially occluded face image, which meets the basic requirements of the current face image recognition.
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Funding
2019 “climbing plan” Guangdong University Student Science and technology innovation and cultivation special fund project, project name: multi pose face image recognition algorithm based on artificial neural network learning, project number: pdjh2019b0619.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lu, Xh., Wang, Lf., Qiu, Jt., Li, J. (2020). A Local Occlusion Face Image Recognition Algorithm Based on the Recurrent Neural Network. 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 326. Springer, Cham. https://doi.org/10.1007/978-3-030-51100-5_14
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DOI: https://doi.org/10.1007/978-3-030-51100-5_14
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