Face Recognition Based on Convolutional Neural Network
DOI:
https://doi.org/10.54097/ekj7m571Keywords:
Face recognition, Convolutional neural network, Gray scaleAbstract
In this paper, we collect the picture data to be processed by gray scale, and use the python environment configured by computer to train and learn the processed training data. Finally, the accuracy rate is above 95% after the model training, which shows that the method of convolutional neural network is very efficient.
Downloads
References
[1] Xiong Xin. Face Recognition Technology and Application [M]. Zhengzhou: Yellow River Water Conservancy Press, 2018.
[2] Huang Xiaoying. Research on Multi-pose Face Recognition Method Based on Subspace [D]. Chongqing University, 2009.
[3] Tang Yongqiang. Application of Linear Projection Analysis Algorithm in Face Recognition [D]. Northeast Petroleum University, 2010.
[4] Chang Xueping. Research on Single Sample Face Recognition Based on Sparse Theory [D]. Zhejiang Normal University, 2011.
[5] Yan Xiaona. Application Research of Support Vector Machine Kernel Method in Face Recognition [D]. Ocean University of China, 2012.
[6] Feiteng. Research on PCA-based Face Recognition [D]. Jilin University, 2016.
[7] Zhang Cheng. Face recognition method based on Tengine edge intelligence [D]. Nanjing University of Posts and Telecommunications, 2020.
[8] Ji Shengbo. Research on Illumination Change Face Recognition Method Based on Generative Adversarial Network and Fuzzy Masking [D]. Xi'an University of Science and Technology, 2021. [9] Lin Miaozhen. Research on Face Recognition Based on Deep Learning [D]. Xidian University, 2013.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Journal of Computing and Electronic Information Management
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
