Contributions to gait recognition using multiple-views

Abstract

This thesis focuses on identifying people by the way they walk. The problem of gait recognition has been addressed by using different approaches, both in the 2D and 3D domains, and using one or multiple views. However, the dependence on camera viewpoint (and therefore the dependence on the trajectory of motion) still remains an open problem. This dissertation addresses the problem of dependence on the trajectory through the use of 3D reconstructions of walking humans. The use of 3D models have several advantages that are worth mentioning. First, by the use of 3D reconstructions it is possible to exploit a greater amount of information in contrast to methods that extract descriptors from just 2D images. Second, the 3D reconstructions can be aligned along the way as if the subject had walked on a treadmill, thus providing a way to recognize people regardless the path. Three approaches are proposed in order to address the dependence on the trajectory: 1) using aligned 3D reconstructions of walking humans; 2) using unaligned 3D reconstructions of walking humans; 3) extracting a 3D description without using 3D reconstructions.