Improved Gait Recognition using Gradient Histogram Gaussian Image

In this paper, we have proposed the incorporation of HOG (Histogram of Oriented Gradients) to Gait Gaussian Image for visibly improved results in gait recognition. This new spatial-temporal representation is called Gradient Histogram Gaussian Image (GHGI). It is almost similar to Gait Energy Image (GEI) but the usage of Gaussian function and further application of HOG considerably increases efficiency and reduces amalgamation of noise. In GEI, silhouettes are averaged and hence only edge information at the boundaries is preserved. Contrary to this, our concept takes the Gaussian distribution over a cycle and computes gradient histograms at all locations. Edge information inside the person silhouette is also preserved this way. The features derived from GHGI are classified using the Nearest Neighbor classifier. The supporting simulations are performed on OU-ISIR database A and B, commonly referred to as the Treadmill database A and B. The potency of our hypothesis is validated with comparative results. (C) 2015 The Authors. Published by Elsevier B.V.