Feature-based Analysis of Gait Signals for Biometric Recognition Automatic Extraction and Selection of Features from Accelerometer Signals

Gait recognition has been traditionally tackled by computer vision techniques. As a matter of fact, this is a still very active research field. More recently, the spreading use of smart mobile devices with embedded sensors has also spurred the interest of the research community for alternative methods based on the gait dynamics captured by those sensors. In particular, signals from the accelerometer seem to be the most suited for recognizing the identity of the subject carrying the mobile device. Different approaches have been investigated to achieve a sufficient recognition ability. This paper proposes an automatic extraction of the most relevant features computed from the three raw accelerometer signals (one for each axis). It also presents the results of comparing this approach with a plain Dynamic Time Warping (DTW) matching. The latter is computationally more demanding, and this is to take into account when considering the resources of a mobile device. Moreover, though being a kind of basic approach, it is still used in literature due to the possibility to easily implement it even directly on mobile platforms, which are the new frontier of biometric recognition.