Authors:
Thang Hoang
1
;
Deokjai Choi
2
and
Thuc Nguyen
3
Affiliations:
1
Faculty of Information Technology and Saigon Technology University, Vietnam
;
2
Chonnam National University, Korea, Republic of
;
3
Faculty of Information Technology and University of Science VNU-HCMC, Vietnam
Keyword(s):
Gait Recognition, Pattern Recognition, Behavioural Biometrics, Implicit Authentication, Accelerometer, Mobile Security.
Related
Ontology
Subjects/Areas/Topics:
Human Factors and Human Behaviour Recognition Techniques
;
Identification, Authentication and Non-Repudiation
;
Information and Systems Security
;
Information Assurance
;
Security and Privacy in Mobile Systems
;
Security Verification and Validation
Abstract:
Authentication schemes using tokens or biometric modalities have been proposed to ameliorate the security
strength on mobile devices. However, the existing approaches are obtrusive since the user is required to
perform explicit gestures in order to be authenticated. While the gait signal captured by inertial sensors is
understood to be a reliable profile for effective implicit authentication, recent studies have been conducted
in ideal conditions and might therefore be inapplicable in the real mobile context. Particularly, the acquiring
sensor is always fixed to a specific position and orientation. This paper mainly focuses on addressing the
instability of sensor’s orientation which mostly happens in the reality. A flexible solution taking advantages
of available sensors on mobile devices which can help to handle this problem is presented. Moreover, a novel
gait recognition method utilizes statistical analysis and supervised learning to adapt itself to the instability of
the biometri
c gait under various circumstances is also proposed. By adopting PCA+SVM to construct the gait
model, the proposed method outperformed other state-of-the-art studies, with an equal error rate of 2.45% and
accuracy rate of 99.14% in terms of the verification and identification aspects being achieved, respectively.
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