Individual identification using a gait dynamics graph

In this paper, we propose a new gait representation-gait dynamics graph (GDG) for individual identification. For each gait sequence, lower limbs joint angles are extracted as gait parameters, and gait system dynamics underlying time-varying gait parameter trajectories is captured by using deterministic learning algorithm. Gait dynamics graph (GDG) is then generated by plotting the extracted dynamics information into three-dimensional graphic. Unlike other gait representations, which are not embedded with dynamics information, GDG demonstrates nonlinear gait dynamics in a new, visually intuitive manner using three-dimensional graphic representation. Both direct matching method and nonlinear dynamics analysis method can be used for GDG recognition independently. The performance of the proposed representation is evaluated and compared with the other representations experimentally on five large benchmark gait databases. This kind of gait representation is embedded with more distinctive information and preserves temporal dynamics information of human walking, which does not rely on shape or silhouettes information. Experimental results show that the GDG representation can further improve recognition rates and avoid the great drop of recognition rate when the training and test sets are under different walking conditions.