Most gait recognition approaches rely on silhouette-based representations due to high recognition accuracy and computational efficiency, and a key problem for those approaches is how to accurately extract individualitypreserved silhouettes from real scenes, where foreground colors may be similar to background colors and the background is cluttered. We therefore propose a method of individuality...

Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. In this paper, an improved method for gait recognition is proposed. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (KPCA), to extract gait features from...

We present a system for rendering novel viewpoints from a set of calibrated and silhouette-segmented images using the visual hull together with multi-view stereo. The visual hull predicted from the object silhouettes is used to restrict the search range of the multi-view stereo. This reduces redundant computation and the possibility of incorrect matches. Unlike previous visual hull approaches, ...

Silhouettes of polyhedra are an important primitive in application areas such as machine vision and computer graphics. In this paper, we study how to select view points of convex polyhedra such that the silhouette satisfies certain properties. Specifically, we give algorithms to find all projections of a convex polyhedron such that a given set of edges, faces and/or vertices appear on the silho...

This paper presents a new approach for human identification at a distance using gait recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four view directions to silhouette. Based on normalized correlation...