Human gait recognition based on deterministic learning through multiple views fusion

• Robust gait recognition via deterministic learning and multiple views fusion.
• Multiple views fusion strategy is employed to form synthesized silhouette.
• Human gait can be characterized with four time-varying gait features.
• Gait variability are effectively modeled via deterministic learning.
• A rapid recognition scheme is chosen for encouraging recognition accuracy.

Gait characteristics extracted from one single camera are limited and not comprehensive enough to develop a robust recognition system. This paper proposes a robust gait recognition method using multiple views fusion and deterministic learning. First, a multiple-views fusion strategy is introduced, in which gaits collected under different views are synthesized as a kind of synthesized silhouette images. Second, the synthesized silhouettes are characterized with four kinds of time-varying gait features, including three width features of the silhouette and one silhouette area feature. Third, gait variability underlying different individuals’ time-varying gait features is effectively modeled by using deterministic learning algorithm. This kind of variability reflects the change of synthesized silhouettes while preserving temporal dynamics information of human walking. Gait patterns are represented as the gait variability underlying time-varying gait features and a rapid recognition scheme is presented in published gait databases. Experimental results show that encouraging recognition accuracy can be achieved.