A Hybrid Gait Recognition Solution Using Video and Ground Contact
Information
Advances in biometric technologies have improved our ability to
uniquely identify individuals based upon physically acquired metrics.
While these methods provide a reliable means of personal
identification, they require a deliberate interaction between the
person in question and the system attempting to establish or verify the
identity (e.g. placing a thumb over a scanning device). This
limitation has motivated researchers to develop methods that allow for
personal identification at a distance. One of the more promising
metrics for distance-based recognition is human gait.
Despite the extensive work that has already been done, gait
recognition technologies have yet to achieve the accuracy and
reliability that is seen with more conventional biometric
identification such as fingerprint recognition. Current methods of
gait recognition either provide too little information about an
individual or they require too much time to compute. The ideal
compromise between current gait recognition systems would be one that
combines both speed and accuracy. Furthermore, it is important for a
recognition system to provide accurate results in a variety of
conditions. Gait information differs from fingerprints in that
movement patterns are more variable under different walking conditions
(e.g. different walking speeds, footwear, surface conditions, etc.).
Accordingly, we propose a recognition system that incorporates both
static and dynamic features for personal identification using both
walking video and ground contact information (GAITRiteTM instrumented
walkway). Given the challenges of creating a robust gait recognition
system, the aims of this study are: (1) to determine the effect of
walking velocity and variable type on the matching performance of
individual video and GAITRiteTM measures; and (2) to determine the
effect of gait velocity on multivariate gait matching performance.
To address these aims, both video and GAITRiteTM data will be
collected from 100 healthy adult participants. A range of static and
dynamic gait and anthropometric variables will be extracted using
techniques developed in preliminary studies. The variables will be
analyzed to determine their individual effectiveness in identifying the
participants. Subsequently, an algorithm will be applied to
systematically identify the most robust multivariate gait recognition
solutions. These solutions will be tested to establish their matching
performance over different gait velocities.
The relevance of this work for both public and private security
applications is obvious. In an age where the threat of terrorist
activities disrupts personal travel and corporate espionage threatens
to cost private companies millions, it is not surprising that great
efforts have been made to develop more unobtrusive personal
identification systems. Human gait may provide the best measure for
uniquely and rapidly identifying individuals at a distance.