Exploiting pose dynamics for human recognition from their gait signatures

Computer vision-based gait recognition has evolved into an active area of research since the past decade, and a number of useful algorithms have been proposed over the years. Among the existing gait recognition techniques, pose-based approaches have gained more popularity due to their inherent capability of capturing the silhouette shape variation during walking at a high resolution. However, a short-coming of the existing pose-based gait recognition approaches is that their effectiveness depends on the accuracy of a pre-defined set of key poses and are, in general, not robust against varying walking speeds. In this work, we propose an improvement to the existing pose-based approaches by considering a gallery of key pose sets corresponding to varying walking speeds instead of just a single key pose set. This gallery is generic and is constructed from a large set of subjects that may/may not include the subjects present in the gait recognition data set. Comparison between a pair of training and test sequences is done by mapping each of these into the individual key pose sets present in the above gallery set, computing the Active Energy Image for each key pose, and next observing the frequency of matched key poses in all the sets. Our approach has been evaluated on two popular gait data sets, namely the CASIA B data and the TUMGAID data. A thorough experimental evaluation along with comparison with state-of-the-art techniques verify the effectiveness of our approach.