ID 113905
Author
Yamamoto, Shun Tokushima University
Keywords
Aerial input numerals
Leap motion
Deep learning
CNN
Dilated convolution
Personal authentication
Content Type
Journal Article
Description
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.
Journal Title
Advances in Science, Technology and Engineering Systems Journal
ISSN
24156698
Publisher
ASTES
Volume
4
Issue
5
Start Page
369
End Page
374
Published Date
2019-10-22
Rights
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License(https://creativecommons.org/licenses/by-sa/4.0/).
EDB ID
DOI (Published Version)
URL ( Publisher's Version )
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology