For citation-. Feng Jun-Long. Design of Face Recognition System Based on Matlabs // Grand Altai Research & Education — Issue 2 (20)'2023 (DOI. 10.25712/ASTU.2410-485X.2023.02) — EDN. https://elibrary.ru/tzsnwg
УДК 004.4274
Design of Face Recognition System Based on Matlabs
Feng Jun-Long1
1 Hubei Digital Textile Equipment Key Laboratory, Wuhan Textile University, Wuhan, 430073, China;
E-mail: [email protected]
Abstract. In order to improve the efficiency of facial recognition and solve the hygiene issues caused by contact, low recognition security, and low recognition efficiency in traditional facial recognition technologies. On the basis of traditional facial recognition technology, this article designs and analyzes a program based on Matlab for image processing and Fourier transform function for facial recognition. This program achieves non-contact, safety, and accuracy in the recognition process. So the design of facial recognition attendance system based on Matlab has high advantages in the field of facial recognition.
Keywords. facial recognition; MATLAB; image processing; Fourier transformation
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[1] £#, mm®, ^m, n«x. matlab mmm^nA^nmrnmn [J]. m^
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[2] n, M#, rnmrn, x«, m »X matlab №Armrnrnm% [J]. i^mmm,
2022(09):69-71.DOI:10. 19769/j.zdhy.2022.09.021.
[3] mrnrn. »X pca m lbp [D]. ^^in^^, 2015.
[4] X&'M, »X matlab [J]. if^Xn^iif, 2018, 39(17): 4639-4642.
[5] »X matlab [J]. EfeX^if S^ffi, 2016, 38(6): 1270-1277.
[6] X^, »X matlab [J]. 2019, 36(2): 38-43.
[7] Younus Fazl-e-Basit Javed f Usman Qayyum, XffiM^ffllA^iHSijmM, References
[1] Xuan Ran, Jiang Mingming, Wang Zhongxiang, Mi Shixin, Liu Hanyu. Design of a Face Access Control System for Epidemic Prevention and Control in MATLAB [J]. Southern Agricultural Machinery, 2021,52 (12): 187-189.
[2] Wang Hui, Huang Rui, Liu Linhui, Xin Fengmei, Li Xin. Research on facial recognition algorithms based on MATLAB [J]. Automation Application, 2022 (09): 69-71. DOI: 1019769/j.zdhy.2022.09.021.
[3] Huo Yanyan Research on facial recognition based on improved PCA and LBP algorithms [D]. Harbin Institute of Technology, 2015.
[4] Zhang Xuegong, Yu Jianbo, Wu Jiannan Research on Visual Cognition Based on Matlab [J] Computer Engineering and Design, 2018, 39 (17): 4639-4642.
[5] Cheng Tianxiang, Si Jun, Su Wenju A Fast Adaptive Principal Component Analysis Face Recognition Algorithm Based on Matlab [J] Journal of Electronics and Information Technology, 2016, 38 (6): 1270-1277.
[6] Wang Feng, Hu Xiaohua, Wei Shiqin A Multi object Face Detection and Tracking System Based on Matlab [J] Computer Applications and Software, 2019, 36 (2): 38-43.
[7] Younus Fazl e-Basit Javed and Usman Qayyum, using histogram for facial recognition and processing, with only relevant emerging technology research reports in the third stage.