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Smartphone Authentication System Using Personal Gaits and a Deep Learning Model.
- Source :
- Sensors (14248220); Jul2023, Vol. 23 Issue 14, p6395, 12p
- Publication Year :
- 2023
-
Abstract
- In a society centered on hyper-connectivity, information sharing is crucial, but it must be ensured that each piece of information is viewed only by legitimate users; for this purpose, the medium that connects information and users must be able to identify illegal users. In this paper, we propose a smartphone authentication system based on human gait, breaking away from the traditional authentication method of using the smartphone as the medium. After learning human gait features with a convolutional neural network deep learning model, it is mounted on a smartphone to determine whether the user is a legitimate user by walking for 1.8 s while carrying the smartphone. The accuracy, precision, recall, and F1-score were measured as evaluation indicators of the proposed model. These measures all achieved an average of at least 90%. The analysis results show that the proposed system has high reliability. Therefore, this study demonstrates the possibility of using human gait as a new user authentication method. In addition, compared to our previous studies, the gait data collection time for user authentication of the proposed model was reduced from 7 to 1.8 s. This reduction signifies an approximately four-fold performance enhancement through the implementation of filtering techniques and confirms that gait data collected over a short period of time can be used for user authentication. [ABSTRACT FROM AUTHOR]
- Subjects :
- GAIT in humans
DEEP learning
CONVOLUTIONAL neural networks
SMARTPHONES
LEARNING
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 23
- Issue :
- 14
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 169711572
- Full Text :
- https://doi.org/10.3390/s23146395