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Optimization of convolutional neural network structure for biometric authentication by face geometry

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成果类型:
期刊论文、会议论文
作者:
Zhengbing Hu;Igor Tereykovskiy;Yury Zorin;Lyudmila Tereykovska;Alibiyeva Zhibek
通讯作者:
Tereykovskiy, I.
作者机构:
[Hu Z.] School of Educational Information Technology, Central China Normal University, Wuhan, China
[Tereykovskiy I.; Zorin Y.] National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine
[Tereykovska L.] Kyiv National University of Construction and Architecture, Kyiv, Ukraine
[Zhibek A.] Kazakh National Research Technical University named after K.I. Satpayev, Almaty, Kazakhstan
通讯机构:
[Tereykovskiy, I.] N
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”Ukraine
语种:
英文
关键词:
Biometric authentication;Convolutional neural network;Facial geometry;Neural network model;Optimization;Recognition
期刊:
Advances in Intelligent Systems and Computing
ISSN:
2194-5357
年:
2019
卷:
754
页码:
567-577
主编:
Hu Z.Dychka I.He M.Petoukhov S.
出版者:
Springer Verlag
ISBN:
9783319910079
基金类别:
This scientific work was financially supported by self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (CCNU16A02015).
机构署名:
本校为第一机构
院系归属:
教育信息技术学院
摘要:
The article presents development of the methodology of using a convolutional neural network for biometric authentication based on the analysis of the user face geometry. The need to create a method of the structural parameters of convolutional neural network adaptation to the expected conditions of its use in a biometric authentication system is postulated. It is proposed to adapt the convolutional neural network structural parameters based on the maximum similarity to the process of recognizing a human face image by an average user considering...

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