Gait Recognition By Walking and Running: A Model-Based Approach

Gait is an emerging biometric for which some techniques, mainly holistic, have been developed to recognise people by their walking patterns. However, the possibility of recognising people by the way they run remains largely unexplored. The new analytical model presented in this paper is based on the biomechanics of walking and running, and will serve as the foundation of an automatic person recognition system that is invariant to these distinct gaits. A bilateral and dynamically coupled oscillator is the key concept underlying this work. Analysis shows that this new model can be used to automatically describe walking and running subjects without parameter selection. Temporal template matching that takes into account the whole sequence of a gait cycle is applied to extract the angles of thigh and lower leg rotation. The phase-weighted magnitudes of the lower order Fourier components of these rotations form the gait signature. Classification of walking and running subjects is performed using the k-nearest-neighbour classifier. Recognition rates are similar to that achieved by other techniques with a similarly sized database. Future work will investigate feature set selection to improve the recognition rate and will determine the invariance attributes, for inter- and intra- class, of both walking and running

Extended Model-Based Automatic Gait Recognition of Walking and Running

Gait is an emerging biometric. Current systems are either holistic or feature based and have been demonstrated to be able to recognise people by the way they walk. This paper describes a new system that extends the feature based approach to recognise people by the way they walk and run. A bilateral symmetric and coupled oscillator is the key concept that underlies this model, which includes both the upper and the lower leg. The gait signature is created from the phase-weighted magnitude of the lower order Fourier components of both the thigh and knee rotation. This technique has proved to be capable of recognising people when walking or running and future work intends to develop invariance attributes of walking or running for the new description

On the Relationship of Human Walking and Running: Automatic Person Identification by Gait

The intimate relationship between human walking and running lies within the skeleto-muscular structure. This is expressed as a mapping that can transform computer vision derived gait signatures from running to walking and vice versa, for purposes of deployment in gait as a biometric or for animation in computer graphics. The computer vision technique can extract leg motion by temporal template matching with a model defined by forced coupled oscillators as the basis. The (biometric) signature is derived from Fourier analysis of the variation in the motion of the thigh and lower leg. These signatures can be used for recognition by running or by walking. In fact, the mapping between these gait modes clusters better than the original signatures (of which running is the more potent) and can be used for recognition purposes alone, or to buttress both of the signatures. Moreover, the two signatures can be made invariant to gait mode by using the new mapping

Automated person recognition by walking and running via model-based approaches

Gait enjoys advantages over other biometrics in that it can be perceived from a distance and is difficult to disguise. Current approaches are mostly statistical and concentrate on walking only. By analysing leg motion we show how we can recognise people not only by the walking gait, but also by the running gait. This is achieved by either of two new modelling approaches which employ coupled oscillators and the biomechanics of human locomotion as the underlying concepts. These models give a plausible method for data reduction by providing estimates of the inclination of the thigh and of the leg, from the image data.Both approaches derive a phase-weighted Fourier description gait signature by automated non-invasive means. One approach is completely automated whereas the other requires specification of a single parameter to distinguish between walking and running. Results show that both gaits are potential biometrics,with running being more potent. By its basis in evidence gathering, this new technique can tolerate noise and low resolution

Performance Analysis on New Biometric Gait Motion Model

Recognising people by the way they walk and/or run is new. A novel analytical model which is invariant to human gait of walking and running is developed based on the concept of dynamically coupled oscillators and the biomechanics of human walking and running. It serves as the foundation of this automatic person recognition system. The effects of noise and low resolution have been evaluated on the largest data set of its kind. This is useful as security camera footage is usually prone to noise and of poor resolution. The gait signature is formed from the Fourier description of the thigh and lower leg rotation. Angles of rotation are extracted via temporal template matching across the whole image sequence. Classification is done via the k-nearest neighbour and cross-validated with the leave-one-out rule. The promising recognition rates for both walking and running suggest the high potential of this technique and using gait as the cue for person identification in practical applications. Future work will focus on understanding the features used to create the gait signature in order to further improve the recognition rate and will determine the invariance attributes for walking and running