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Avtomatika i Telemekhanika, 2022, Issue 10, Pages 94–104
DOI: https://doi.org/10.31857/S0005231022100099 (Mi at16054) 		 
This article is cited in 5 scientific papers (total in 5 papers)

Topical issue

Real-time person identification by video image based on YOLOv2 and VGG 16 networks

A. V. Bobkov, Kh. Aung

Bauman Moscow State Technical University, Moscow, 105005 Russia
Full-text PDF (1145 kB) First page Citations (5)
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DOI: https://doi.org/10.31857/S0005231022100099
Abstract: This paper deals with the problem of video-based face recognition. Nowadays, facial recognition methods have made a big step forward, but video-based recognition with its poor quality, difficult lighting conditions, and real-time requirements is still a difficult and unfinished task.
The paper uses the apparatus of convolutional networks for various stages of processing: for capturing and detecting a face, for constructing a feature vector, and finally for recognition. All algorithms are implemented and studied in the Matlab environment to simplify their further export to embedded applications.
Keywords: VGG16 convolutional neural network, face recognition, YOLOv2 object detection algorithm, deep learning, face database.
Presented by the member of Editorial Board: A. A. Lazarev

Received: 17.02.2022
Revised: 22.04.2022
Accepted: 29.06.2022
English version:
Automation and Remote Control, 2022, Volume 83, Issue 10, Pages 1567–1575
DOI: https://doi.org/10.1134/S00051179220100095
Bibliographic databases:
Document Type: Article
Language: Russian
Citation: A. V. Bobkov, Kh. Aung, “Real-time person identification by video image based on YOLOv2 and VGG 16 networks”, Avtomat. i Telemekh., 2022, no. 10, 94–104; Autom. Remote Control, 83:10 (2022), 1567–1575
Citation in format AMSBIB
\Bibitem{BobAun22}
\by A.~V.~Bobkov, Kh.~Aung
\paper Real-time person identification by video image based on YOLOv2 and VGG 16 networks
\jour Avtomat. i Telemekh.
\yr 2022
\issue 10
\pages 94--104
\mathnet{http://mi.mathnet.ru/at16054}
\crossref{https://doi.org/10.31857/S0005231022100099}
\edn{https://elibrary.ru/AKSNXL}
\transl
\jour Autom. Remote Control
\yr 2022
\vol 83
\issue 10
\pages 1567--1575
\crossref{https://doi.org/10.1134/S00051179220100095}
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  • https://www.mathnet.ru/eng/at/y2022/i10/p94
    • This publication is cited in the following 5 articles:
    Citing articles in Google Scholar: Russian citations, English citations
    Related articles in Google Scholar: Russian articles, English articles
    		Avtomatika i Telemekhanika
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    Abstract page:85
    References:22
    First page:17
     
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