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ID 113905
著者
Yamamoto, Shun Tokushima University
キーワード
Aerial input numerals
Leap motion
Deep learning
CNN
Dilated convolution
Personal authentication
資料タイプ
学術雑誌論文
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
ISSN
24156698
出版者
ASTES
4
5
開始ページ
369
終了ページ
374
発行日
2019-10-22
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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出版社版DOI
出版社版URL
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言語
eng
著者版フラグ
出版社版
部局
理工学系