Research Article
A Local Occlusion Face Image Recognition Algorithm Based on the Recurrent Neural Network
@INPROCEEDINGS{10.1007/978-3-030-51100-5_14, author={Xing-hua Lu and Ling-feng Wang and Ji-tao Qiu and Jing Li}, title={A Local Occlusion Face Image Recognition Algorithm Based on the Recurrent Neural Network}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part I}, proceedings_a={ICMTEL}, year={2020}, month={7}, keywords={Recurrent neural network Partially occluded face image Recognition algorithm}, doi={10.1007/978-3-030-51100-5_14} }- Xing-hua Lu
Ling-feng Wang
Ji-tao Qiu
Jing Li
Year: 2020
A Local Occlusion Face Image Recognition Algorithm Based on the Recurrent Neural Network
ICMTEL
Springer
DOI: 10.1007/978-3-030-51100-5_14
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.
