Feature selection on Gait Energy Image for human identification

In this paper we address the problem of selecting the most relevant features for human identification by gait. Although gait as a behavioral biometric is concerned with how people walk rather than how people look, most existing gait recognition approaches employ both shape and dynamics information for recognition. This is because shape, as a static appearance feature also contains useful information for identification. However, the inclusion of shape information in the gait features can also introduce variations that will hinder the recognition performance. To address this problem, we develop both supervised and unsupervised feature selection methods to extract the most relevant and informative features from Gait Energy Image (GEI) for human identification. Extensive experiments are carried out which indicate that our feature selection methods significantly improve the performance of gait recognition.