Model-Based Approaches for Predicting Gait Changes over Time

摘要

Interest in automated biometrics continues to increase, but has little consideration of time. This paper deals with a problem of recognition by gait when time-dependent and time-invariant covariates are added. We have shown previously how recognition rates fall significantly for data captured over lengthy time intervals. We suggest predictive models of changes in gait due both to time and now to time-invariant covariates. A considerable improvement in recognition capability is demonstrated, with potential generic biometric application.