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Abstract

The dialing-type authentication as a common PIN code input system has gained popularity due to the simple and intuitive design. However, this type of system has the security risk of “shoulder surfing attack”, so that attackers can physically view the device screen and keypad to obtain personal information. Therefore, based on the use of “Leap Motion” device and “Media Pipe” solutions, in this paper, we try to propose a new two-factor dialing-type input authentication system powered by aerial hand motions and features without contact. To be specific, based on the design of the aerial dialing system part, as the first authentication part, we constructed a total of two types of hand motion input subsystems using Leap Motion and Media Pipe, separately. The results of FRR (False Rejection Rate) and FAR (False Acceptance Rate) experiments of the two subsystems show that Media Pipe is more comprehensive and superior in terms of applicability, accuracy, and speed. Moreover, as the second authentication part, the user’s hand features (e.g., proportional characteristics associated with fingers and palm) were used for specialized CNN-LSTM model training to ultimately obtain a satisfactory accuracy.

Details

1009240
Company / organization
Title
A Deep-Learning-Driven Aerial Dialing PIN Code Input Authentication System via Personal Hand Features
Author
Wang, Jun 1 ; Wang, Haojie 2 ; Sato, Kiminori 3 ; Wu, Bo 3   VIAFID ORCID Logo 

 Graduate School of Bionics, Computer and Media Sciences, Tokyo University of Technology, Hachioji 192-0982, Tokyo, Japan; [email protected] 
 School of Information Engineering, Chang’an University, Xi’an 710021, China; [email protected] 
 School of Computer Science, Tokyo University of Technology, Hachioji 192-0982, Tokyo, Japan; [email protected] 
Publication title
Volume
14
Issue
1
First page
119
Publication year
2025
Publication date
2025
Publisher
MDPI AG
Place of publication
Basel
Country of publication
Switzerland
Publication subject
e-ISSN
20799292
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
 
 
Online publication date
2024-12-30
Milestone dates
2024-11-24 (Received); 2024-12-29 (Accepted)
Publication history
 
 
   First posting date
30 Dec 2024
ProQuest document ID
3153799200
Document URL
https://www.proquest.com/scholarly-journals/deep-learning-driven-aerial-dialing-pin-code/docview/3153799200/se-2?accountid=208611
Copyright
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-01-17
Database
ProQuest One Academic