Frontal View-Based Gait Identification Using Largest Lyapunov Exponents

This paper features two novel approaches to gait recognition; one is frontal motion analysis, using a single camera. This allows the use of other biometrics easily. Second is analysing gait using of nonlinear dynamics of time series, normally used in chaos theory, for classification. A set of point light sources attached to various points of a walking person allows the walker to be identified. Phase-space analysis of trajectories of these moving light displays (MLDs) provides sufficient information for identification of people by their gait. Using chaotic measures to identify humans by their gait sets a significant precedent