Information fusion from multiple cameras for gait-based re-identification and recognition

In this study, the authors present a fully automated frontal (i.e. employing front and back views only) gait recognition approach using the depth information captured by multiple Kinect RGB-D cameras. Limited depth sensing range restricts each of these Kinects to record only a part of a complete gait cycle of a walking subject. Hence, information from more than one Kinect is fused together to examine which features of a gait cycle can be conveniently extracted from the sequences captured independently by these cameras. To achieve this, it is imperative that the same subject be re-identified as he moves from the field of view of one camera to another. The authors use a set of soft-biometric features computed from the skeleton stream provided by Kinect software development kit) for doing automatic re-identification. To enable such information fusion and also to handle missing components even after re-identification, features are extracted at the granularity of small fractions of a gait cycle. Experiments carried out on a data set with gait videos captured by Kinects respectively from the back and front views show promising results.