Gait energy volumes and frontal gait recognition using depth images

Abstract

Gait energy images (GEIs) and its variants form the basis
of many recent appearance-based gait recognition systems.
The GEI combines good recognition performance with a simple implementation, though it suffers problems inherent to appearance-based approaches, such as being highly view dependent. In this paper, we extend the concept of the GEI to 3D, to create what we call the gait energy volume, or GEV. A basic GEV implementation is tested on the CMU MoBo database, showing improvements over both the GEI baseline and a fused multi-view GEI approach. We also demonstrate the efficacy of this approach on partial volume reconstructions created from frontal depth images, which
can be more practically acquired, for example, in biometric
portals implemented with stereo cameras, or other depth
acquisition systems. Experiments on frontal depth images
are evaluated on an in-house developed database captured
using the Microsoft Kinect, and demonstrate the validity of
the proposed approach.