Abstract.
Gait analyses have recently gained attention as methods of identification of individuals at a distance from a camera. However, ap- pearance changes due to view direction changes cause diffculties for gait recognition systems. Here, we propose a method of gait recognition from various view directions using frequency-domain features and a view transformation model. We first construct a spatio-temporal silhouette volume of a walking person and then extract frequency-domain features of the volume by Fourier analysis based on gait periodicity. Next, our view transformation model is obtained with a training set of multiple persons from multiple view directions. In a recognition phase, the model transforms gallery features into the same view direction as that of an in- put feature, and so the features match each other. Experiments involving gait recognition from 24 view directions demonstrate the effectiveness of the proposed method.