Using gait as a biometric is of emerging interest. We describe a new model-based moving feature extraction analysis is presented that automatically extracts and describes human gait for recognition. The gait signature is extracted directly from the evidence gathering process. This is possible by using a Fourier series to describe the motion of the upper leg and apply temporal evidence gathering techniques to extract the moving model from a sequence of images. Simulation results highlight potential performance benefits in the presence of noise. Classification uses the k-nearest neighbour rule applied to the Fourier components of the motion of the upper leg. Experimental analysis demonstrates that an improved classification rate is given by the phase-weighted Fourier magnitude information over the use of the magnitude information alone. The improved classification capability of the phase-weighted magnitude information is verified using statistical analysis of the separation of clusters in the feature space. Furthermore, the technique is shown to be able to handle high levels of occlusion, which is of especial importance in gait as the human body is self-occluding. As such, a new technique has been developed to automatically extract and describe a moving articulated shape, the human leg, and shown its potential in gait as a biometric.

Gait is a biometric which is subject to increasing interest. Current approaches include modelling gait as a spatio-temporal sequence and as an articulated model. By considering legs only, gait can be considered to be the motion of interlinked pendula. We describe how the Hough transform is used to extract the lines which represent legs in sequences of video images. The change in inclination of these lines follows simple harmonic motion; this motion is used as the gait biometric. The method of least squares is used to smooth the data and to infill for missing points. Then, Fourier transform analysis is used to reveal the frequency components of the change in inclination of the legs. The transform data is then classified using the k-nearest neighbour rule. Experimental analysis shows how phase-weighted Fourier magnitude spectra afford an improved classification rate over use of just magnitude spectra. Accordingly, it appears that it is not just the frequency content which makes gait a practical biometric, but its phase as well.

Gait is an emergent biometric aimed essentially to recognise people by the way they walk. Its advantages are that it is non-invasive and that it is less likely to be obscured since it appears to be difficult to camouflage, especially in cases of serious crime. Gait has allied subjects which lend support to the view that gait has clear potential as a biometric. Essentially, we use computer vision to find people and to derive a gait signature from a sequence of images. The majority of current approaches derive motion characteristics, which are then used for recognition. Early results by these studies confirm that there is a rich potential in gait for recognition. Only continued development in technique and in analysis will confirm whether its performance can match that of other biometrics.

Gait is an emergent biometric aimed essentially to recognise people by the way they walk. Its advantages are that it is non-invasive and that it is less likely to be obscured since it appears to be difficult to camouflage, especially in cases of serious crime. Gait has allied subjects which lend support to the view that gait has clear potential as a biometric. Essentially, we use computer vision to find people and to derive a gait signature from a sequence of images. The majority of current approaches derive motion characteristics, which are then used for recognition. Early results by these studies confirm that there is a rich potential in gait for recognition. Only continued development in technique and in analysis will confirm whether its performance can match that of other biometrics.