Gait is a new biometric aimed to recognise a subject by the manner in which they walk. Gait has several advantages over other biometrics, most notably that it is non-invasive and perceivable at a distance when other biometrics are obscured. We present a new area based metric, called gait masks, which provides statistical data intimately related to the gait of the subject. This provides the first statistical approach that can expose the dynamics of the change in area of a subject. Early results show promising results with a recognition rate of 90% on a standard database. Further, there appear to be performance advantages with respect to handling of noise associated with this new approach, together with capability for extension and generalisation. Future research will capitalise on the advantages of this new approach, together with analysis on a larger database.
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This document produced for BMVC 2001