Using gait as a biometric, via phase-weighted magnitude spectra

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Description/Abstract

Gait is a biometric which is subject to increasing interest. Current approaches include modelling gait as a spatio-temporal sequence and as an articulated model. By considering legs only, gait can be considered to be the motion of interlinked pendula. We describe how the Hough transform is used to extract the lines which represent legs in sequences of video images. The change in inclination of these lines follows simple harmonic motion; this motion is used as the gait biometric. The method of least squares is used to smooth the data and to infill for missing points. Then, Fourier transform analysis is used to reveal the frequency components of the change in inclination of the legs. The transform data is then classified using the k-nearest neighbour rule. Experimental analysis shows how phase-weighted Fourier magnitude spectra afford an improved classification rate over use of just magnitude spectra. Accordingly, it appears that it is not just the frequency content which makes gait a practical biometric, but its phase as well.