Clothes-invariant gait identification using part-based adaptive weight control

This paper describes a method of part-based gait identification under substantial clothes variations. When clothes types between a gallery and a probe are different, silhouettes fairly change for some parts and subject discrimination capability decrease for those parts. Therefore, we exploit the discrimination capability as a matching weight for each part and control the weights adaptively based on a distribution of distances between a probe and all the galleries. As a result of experiments with our clothes-variation gait dataset, the proposed method achieved much better performance than a whole-based approach.