Abstract
In the face recognition system on campus, the influence of time and age change on face features can be neglected. This paper proposes a dimension reduction algorithm based on principal component analysis (PCA) algorithm and local binary patterns (LBP), and it is applied to campus face recognition APP. It is proved that the algorithm can significantly improve the speed and ensure its recognition accuracy in the application of small changes in the face and has certain reference value for practical application.
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References
Xu, J.Z., Wu, Z.H., Xu, Y. et al.: Face recognition based on PCA, LDA and SVM algorithms. Comput. Eng. Appl. 1–6 (2019)
Li, M.X., Yao, S.Y.: Design and improvement of face recognition system based on PCA. Comput. Sci. 46(S1), 577–579 (2019)
Wang, W.Q., Gao, G.K.: Cross-age face recognition based on PCA and SVM classification. Comput. Era (7) (2019)
Yu, F.J., Zhang, B., Jia, B.L.: A model of face recognition based on feature location and PCA algorithm to reduce the interference of age factors. Electron. Qual. 6, 79–86 (2019)
Dong, E.Z., Wei, K.X., Yu, X., Feng, Q.: A model recognition algorithm integrating PCA into LBP feature dimension reduction. Comput. Eng. Sci. 39(2), 359–363 (2017)
Zhao, H.H., Liu, H.: Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. Granul. Comput. (2019)
Zhao, H.H., Rosin, P., Lai, Y.K.: Image neural network style transfer with global and local optimization fusion. IEEE Access (2019)
Zhao, H.H., Rosin, P., Lai, Y.K. Zheng, J.H., Wang, Y.N.: Adaptive gradient-based block compressive sensing with sparsity for noisy images. Multimed. Tools Appl. (2019)
Zhao, H., Rosin, P.L., Lai, Y.K.: Automatic semantic style transfer using deep convolutional neural networks and soft masks. Vis. Comput. (2019)
Hu, C.S., Ding, D.H.: Multiplayer target dynamic face recognition. Mod. Comput. (Prof. Ed.) 30, 7–10 (2016)
Zhu, L., Hu, T., Luo, F., Mao, L., Ji, Z.Y.: Identity authentication base on adaptive LBP face recognition. Comput. Eng. Sci. 36(11), 2217–2222 (2014)
Wang, W., Huang, F.F., Li, J.W., Feng, H.L.: Face description and recognition by LBP pyramid. J. Comput. Aided Des. Comput. Graph ICS 1, 301–304 (2009)
Ma, X.L., Liu, Q., Hu, K.Y.: MATLAB Image Processing. China Railway Publishing House, Beijing, pp. 189–191 (2013)
Acknowledgements
This work was supported by Application-oriented Special Disciplines Double First-Class University Project of Hunan Province (Xiangjiaotong [2018] 469), Hunan University Students’ Research Learning and Innovative Experiment Project (Hunan Education Tong [2018] 255:750) and National Innovation and Entrepreneurship Training Program for College Students (201810546007).
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Li, K., Nie, R. (2021). Research on Face Recognition Algorithms and Application Based on PCA Dimension Reduction and LBP. In: Liu, Q., Liu, X., Li, L., Zhou, H., Zhao, HH. (eds) Proceedings of the 9th International Conference on Computer Engineering and Networks . Advances in Intelligent Systems and Computing, vol 1143. Springer, Singapore. https://doi.org/10.1007/978-981-15-3753-0_45
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DOI: https://doi.org/10.1007/978-981-15-3753-0_45
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