Essentially, gait masks are used to derive information from a sequence of silhouettes. This information is directly related to the gait of the subject. The table below shows example gait masks and silhouettes from the sequences to which they are applied. The gait masks measure how the silhouette changes over time in a chosen region of the body. These area changes are intimately related to the nature of gait.
Application shows the sinusoidal nature of the output from a vertical line mask.
The peaks in the graph correspond to when the legs are closest together, and the dips represent when the legs are at furthest flexion. Using Canonical Analysis it is possible to use this output for recognition purposes. Future work will concentrate on combining results from different gait masks with the aim of increasing the overall recognition rate. In addition, the performance of the system on a much larger database of subjects will be evaluated.