A Predictive Model for Gait Recognition

Gait Recognition has been paid an attention to as non-contact and unobtrusive biometric method. Magnitude and phase spectra of horizontal and vertical movement of ankles in a normal walk are effective and efficient signatures in gait recognition. However, gait recognition rate degrades significantly due to variance caused by covariates of clothing, surface or time lapse. In this paper, to improve gait recognition rate on a variety of footwear, a predictive model is proposed. The predictive model is able to estimate slipper gait from shoes gait. By using predictive slipper gait, much higher recognition rate is achieved for slipper gait over time lapse than ones without predictive model. The predictive model designed in this paper succeeds in separation of the variance due to a footwear covariate from the variance due to a time covariate.