Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning

For obtaining optimal performance, as many informative cues as possible should be involved in the gait recognition algorithm. This paper describes a gait recognition algorithm by combining spatial-temporal and kinematic gait features. For each walking sequence, the binary silhouettes are characterized with four time-varying spatial-temporal parameters, including three lower limbs silhouette widths and one holistic silhouette area. Using deterministic learning algorithm, spatial-temporal gait features can be represented as the gait dynamics underlying the trajectories of lower limbs silhouette widths and holistic silhouette area, which can implicitly reflect the temporal changes of silhouette shape. In addition, a model-based method is proposed to extract joint-angle trajectories of lower limbs. Kinematic gait features can be represented as the gait dynamics underlying the trajectories of joint angles, which can represent the temporal changes of body structure and dynamics. Both spatial-temporal and kinematic cues can be used separately for gait recognition using smallest error principle. They are fused on the decision level using different combination rules to improve the gait recognition performance. The fusion of two different kinds of features provides a comprehensive characterization of gait dynamics, which is not sensitive to the walking conditions variation. The proposed method can still achieve superior performance when the testing walking conditions are different from the corresponding training conditions. Experimental results show that encouraging recognition accuracy can be achieved on five public gait databases: CASIA-B, CASIA-C, TUM GAID, OU-ISIR, USF HumanID.