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.

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We present a new evidence gathering based approach, aimed to extract moving articulated objects from a temporal sequence of images. The new technique is designed to enable the automated determination of parameters pertaining to human gait, with a view to possible use as a biometric for recognition purposes. The articulated line feature extraction technique, uses a genetic algorithm (GA) based implementation of the Velocity Hough Transform (VHT). Using a GA to perform a heuristic search of the parameter space, rather than an exhaustive one, overcomes the problems of computation time and memory requirements associated with the original approach. The new technique employs a parametric gait model consisting of a pair of articulated lines, jointed at the hip. Trials on real image sequences of pedestrians demonstrate that the approach is capable of locating and tracking a walking subject. Moreover, the technique is able to provide reasonable estimates of an individual's gait cycle period and hip rotation patterns, which are pertinent to recognition. However, current levels of accuracy are insufficient for these purposes. Nevertheless, the results demonstrate that the articulated line feature extraction technique has potential for use as an automated gaitdata retrieval system.

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