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.
Highlights
During the last decade, smartphones have become a ubiquitous technology, with more than 6.3 billion users currently around the world (Statista, 2021)
In the authors’ prior work (AlObaidi et al, 2018) utilising the controlled data, the results demonstrated that a dynamic feature vector of between 10-160 features provided the best classification performance and presents an interesting insight into the realisation of a gait recognition scheme, moving from controlled data to actual real-life data
The results would suggest, a longer feature vector is beneficial in such circumstances, with the possible effect of helping to mitigate against the impacts of larger signal variations that exist in real-life data
Summary
Smartphones have become a ubiquitous technology, with more than 6.3 billion users currently around the world (Statista, 2021). Smartphones provide a wide range of services and features (e.g. personal communications, entertainment, and business) and are used to access and store sensitive and confidential information such as financial data and health-based records. More recent research has explored the use of personal devices, both wearable sensors and smartphones, to enable or facilitate local user authentication. One of the most extensive gait datasets, which are publicly accessible is from Osaka University (Ngo et al, 2014) It is based on three internal sensors placed on the subjects’ belt, with a triaxle accelerometer and gyroscope. The collected data consists of 744 subjects, it was collected in a controlled environment, and for each participant, there are only two data sequences available (each session lasting about 1 minute) With such limited signals for each individual, it is challenging to validate the approach extensively.
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