Feature Selection for Gait Recognition without Subject Cooperation

The strength of gait, compared to other biometrics, is that it does not require cooperative subjects. Previoius gait recognition approaches were evaluated using a gallery set consisting of gait sequences of people under similar covariate conditions (i.e. clothing, surface, carrying, and view conditions). This evaluation procedure, however, implies that the gait data are collected in a cooperative manner so that the covariate conditions are known a priori. In this work, the performance of state of the art gait recognition approaches are evaluated without the assumption on cooperative subjects, i.e. the gallery set consists of a mixture of gait sequences under different unknown covariate conditions. The results show that the performance of the existing approaches drop drastically under this more realistic experimental setup. We argue that selecting the most relevant gait features that are invariant to changes in gait covariate conditions is the key to develop a gait recognition system that works without subject cooperation. To that end, we propose a novel gait recognition approach, which performs automatic feature selection on each pair gallery and probe gait sequences, and seamlessly integrates feature selection with an Adaptive Component and Discriminant Analysis (ACDA) for fast recognition. Experiments are carried out to demonstrate that the proposed approach significantly outperforms the existing techniques.