The effect of time on the performance of gait biometrics

Many studies have shown that it is possible to recognize people by the way they walk. However, there are a number of covariate factors that affect recognition performance. The time between capturing the gallery and the probe has been reported to affect recognition the most. To date, no study has shown the isolated effect of time, irrespective of other covariates. Here we present the first principled study that examines the effect of elapsed time on gait recognition. Using empirical evidence we have shown for the first time that elapsed time does not affect recognition significantly in the short to medium term. By controlling clothing, a Correct Classification Rate (CCR) of 95% has been achieved over 9 months, on a dataset of nearly 2000 gait sequences/samples. We have created a new multimodal temporal database to enable the research community to investigate various gait and face covariates in a formal manner. Our results show that gait can be used as a reliable biométrie over time and at a distance. We have demonstrated that clothing drastically affects performance regardless of elapsed time. A move towards developing appearance invariant recognition algorithms is essential.