ID | 113905 |
著者 |
Yamamoto, Shun
Tokushima University
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キーワード | Aerial input numerals
Leap motion
Deep learning
CNN
Dilated convolution
Personal authentication
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資料タイプ |
学術雑誌論文
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抄録 | With the progress of IoT, everything is going to be connected to the network. It will bring us a lot of benefits however some security risks will be occurred by connecting network. To avoid such problems, it is indispensable to strengthen security more than now. We focus on personal authentication as one of the security.
As a security enhancement method, we proposed a method to carry out numeral identification and personal authentication using numerals written in the air with Leap motion sensor. In this paper, we also focus on proper handling of aerial input numerals to verify whether the numerals written in the air are helpful for authentication. We collect numerals 0 to 9 from five subjects, then apply three pre-processing to these data, learn and authenticate them by CNN (convolutional neural network) which is a method of machine learning. As a result of learning, an average authentication accuracy was 92.4%. This result suggests that numerals written in the air are possible to carry out personal authentication and it will be able to construct a better authentication system. |
掲載誌名 |
Advances in Science, Technology and Engineering Systems Journal
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ISSN | 24156698
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出版者 | ASTES
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巻 | 4
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号 | 5
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開始ページ | 369
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終了ページ | 374
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発行日 | 2019-10-22
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権利情報 | This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License(https://creativecommons.org/licenses/by-sa/4.0/).
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言語 |
eng
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著者版フラグ |
出版社版
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部局 |
理工学系
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