Gait as a motion-based biometric has the merit of being non-contact and unobtrusive. In this paper, we proposed a gait recognition approach using spectral features of horizontal and vertical movement of ankles in a normal walk. Gait recognition experiments using the spectral features in term of the magnitude, phase and phase-weighted magnitude show that both magnitude and phase spectra are effective gait signatures, but magnitude spectra are slightly superior. We also proposed the use of geometrical mean based spectral features for gait recognition. Experimental results with 9 subjects show encouraging results in the same-day test, while the effect of time covariate is confirmed in the cross-month test.
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