Gait Identification Based on Multi-view Observations Using Omnidirectional Camera

Kazushige Sugiura, Yasushi Makihara, Yasushi Yagi

Abstract: We propose a method of gait identification based on multi-view gait images using an omnidirectional camera. We first transform omnidirectional silhouette images into panoramic ones and obtain a spatio-temporal Gait Silhouette Volume (GSV). Next, we extract frequency- domain features by Fourier analysis based on gait periods estimated by autocorrelation of the GSVs. Because the omnidirectional camera makes it possible to observe a straight-walking person from various views, multi-view features can be extracted from the GSVs composed of multi-view images. In an identification phase, distance between a probe and a gallery feature of the same view is calculated, and then these for all views are integrated for matching. Experiments of gait identification including 15 subjects from 5 views demonstrate the effectiveness of the proposed method.