Exploiting gradient histograms for gait-based person identification

In this paper, we exploit gradient histograms for person identification based on gait. A traditional and successful method for gait recognition is the Gait Energy Image (GEI). Here, person silhouettes are averaged over full gait cycles, which leads to a robust and efficient representation. However, binarized silhouettes only capture edge information at the boundary of the person. By contrast, the Gradient Histogram Energy Image (GHEI) also captures edges within the silhouette by means of gradient histograms. Combined with precise α-matte preprocessing and with a new part-based extension, recognition performance can be further improved. In addition, we show, that GEI can even be outperformed by directly applying gradient histogram extraction on the already bina-rized silhouettes. We run all experiments on the widely used HumanID gait database and show significant performance improvements over the current state of the art.