Improved Gait Recognition using Gradient Histogram Energy Image

We present a new spatio-temporal representation for Gait Recognition, which we call Gradient Histogram Energy Image (GHEI). Similar to the successful Gait Energy Image (GEI), information is averaged over full gait cycles to reduce noise. Contrary to GEI, where silhouettes are averaged and thus only edge information at the boundary is used, our GHEI computes gradient histograms at all locations of the original image. Thus, also edge information inside the person silhouette is captured. In addition, we show that GHEI can be greatly improved using precise segmentation techniques (we use α-matte segmentation). We demonstrate great effectiveness of GHEI and its variants in our experiments on the large and widely used HumanID Gait Challenge dataset. On this dataset we reach a significant performance gain over the current state of the art.