Paper Info
Title
HandiText: Handwriting Recognition Based on Dynamic Characteristics with Incremental LSTM
Abstract
The Internet of Things (IoT) is a new manifestation of data science. To ensure the credibility of data about IoT devices, authentication has gradually become an important research topic in the IoT ecosystem. However, traditional graphical passwords and text passwords can cause user’s serious memory burdens. Therefore, a convenient method for determining user identity is needed. In this article, we propose a handwriting recognition authentication scheme named HandiText based on behavior and biometrics features. When people write a word by hand, HandiText captures their static biological features and dynamic behavior features during the writing process (writing speed, pressure, etc.). The features are related to habits, which make it difficult for attackers to imitate. We also carry out algorithms comparisons and experiments evaluation to prove the reliability of our scheme. The experiment results show that the Long Short-Term Memory has the best classification accuracy, reaching 99% while keeping relatively low false-positive rate and false-negative rate. We also test other datasets, the average accuracy of HandiText reach 98%, with strong generalization ability. Besides, the 324 users we investigated indicated that they are willing to use this scheme on IoT devices.
Year
DOI
Venue
2020
10.1145/3385189
ACM/IMS Transactions on Data Science
Keywords
DocType
Volume
Authentication,Data Science,Handwriting,Internet of Things,LSTM
Journal
1
Issue
ISSN
Citations 
4
2691-1922
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Liming Fang132328.85
Hongwei Zhu201.35
Boqing Lv300.34
Zhe Liu428754.56
Weizhi Meng534056.49
Hong Yu61982179.13
Shouling Ji761656.91
Ze-Hong Cao89615.40