Differential Radon Transform for gait recognition

Experimental studies have proved that high frequency components have the maximum contribution in silhouette-based gait recognition. The Radon Transform (RT), used in gait analysis for its ability to compute useful directional projections, fails to capture the necessary high frequency content of images. In this paper we present the Differential Radon Transform (DiffRT) - a novel adaptation of the standard RT designed to extract such high frequency information efficiently. The proposed transform is used to extract a set of features from gait silhouettes. We provide both theoretical and experimental evidence that DiffRT can indeed collect the important image information to facilitate gait-based human recognition. Averaged silhouettes from USF database are used for performance evaluation following the gait challenge framework. Our proposed method achieves high recognition accuracy and outperforms several state-of-the-art algorithms.