Expanding gait identification methods from straight to curved trajectories

Conventional methods of gait analysis for person identification use features extracted from a sequence of camera images taken during one or more gait cycles. An implicit assumption is made that the walking direction does not change. However, cameras deployed in real-world environments (and often placed at corners) capture images of humans who walk on paths that, for a variety of reasons, such as turning corners or avoiding obstacles, are not straight but curved. This change of the direction of the velocity vector causes a decrease in performance for conventional methods. In this paper we address this aspect, and propose a method that offers improved identification results for people walking on curved trajectories. The large diversity of curved trajectories makes the collection of complete real world data infeasible. The proposed method utilizes a 4D gait database consisting of multiple 3D shape models of walking subjects and adaptive virtual image synthesis. Each frame, for the duration of a gait cycle, is used to estimate a walking direction for the subject, and consequently a virtual image corresponding to this estimated direction is synthesized from the 4D gait database. The identification uses affine moment invariants as gait features. Experiments using the 4D gait database of 21 subjects show that the proposed method has a higher recognition performance than conventional methods.