Spatio-temporal Energy Based Gait Recognition

Recently there has been lot of interest in using the gait energy image (GEI) of human walk sequence for individual recognition. Researchers have reported very good recognition rates using both unsupervised and supervised methods for normal walk sequences. However, the performance degrades when there is a variant like change in clothing or carrying a bag. This paper shows that the performance for the variant situations can be improved by constructing the GEI with sway alignment instead of upper body alignment, and dynamically selecting just the required number of rows from the bottom of the silhouette as inputs for an unsupervised feature selection approach. The improvement in recognition rates are established with performance testing on a large gait dataset.