  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
biometric 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.
