On Clustering Human Gait Patterns

Research in automated human gait recognition has largely focused on developing robust feature representation and matching algorithms. In this paper, we investigate the possibility of clustering gait patterns based on the features extracted by automated gait matchers. In this regard, a k-means based clustering approach is used to categorize the feature sets extracted by three different gait matchers. Experiments are conducted in order to determine if (a) the clusters of identities corresponding to the three matchers are similar, and (b) if there is a correlation between gait patterns within each cluster and physical attributes such as gender, body area, height, stride, and cadence. Results demonstrate that human gait patterns can be clustered, where each cluster is defined by identities sharing similar physical attributes. In particular, body area and gender are found to be the primary attributes captured by gait matchers to assess similarity between gait patterns. However, the strength of the correlation between clusters and physical attributes is different across the three matchers, suggesting that gait matchers "weight" attributes differently. The results of this study should be of interest to gait recognition and identification-at-a-distance researchers.