Document Type
Journal Article
Publication Title
Computers & Security
Volume
113
Publisher
Elsevier
School
School of Science / ECU Security Research Institute
RAS ID
52206
Abstract
As the smartphone and the services it provides are becoming targets of cybercrime, it is critical to secure smartphones. However, it is important security controls are designed to provide continuous and user-friendly security. Amongst the most important of these is user authentication, where users have experienced a significant rise in the need to authenticate to the device and individually to the numerous apps that it contains. Gait authentication has gained attention as a mean of non-intrusive or transparent authentication on mobile devices, capturing the information required to verify the authenticity of the user whilst the person is walking. Whilst prior research in this field has shown promise with good levels of recognition performance, the results are constrained by the gait datasets utilised being based upon highly controlled laboratory-based experiments which lack the variability of real-life environments. This paper introduces an advanced real-world smartphone-based gait recognition system that recognises the subject within real-world unconstrained environments. The proposed model is applied to the uncontrolled gait dataset, which consists of 44 users over a 7–10 day capture – where users were merely asked to go about their daily activities. No conditions, controls or expectations of particular activities were placed upon the participants. The experiment has modelled four types of motion normal walking, fast walking and down and upstairs for each of the users. The evaluation of the proposed model has achieved an equal error rate of 11.38%, 11.32%, 24.52%, 27.33% and 15.08% for the normal, fast, down and upstairs and all activities respectively. The results illustrate, within an appropriate framework, that gait recognition is a viable technique for real-world use.
DOI
10.1016/j.cose.2021.102557
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Alobaidi, H., Clarke, N., Li, F., & Alruban, A. (2022). Real-world smartphone-based gait recognition. Computers & Security, 113, article 102557.
https://doi.org/10.1016/j.cose.2021.102557