Improving human gait recognition using feature selection

Abstract

Human gait, a biometric aimed to recognize individuals by the way they walk has recently come to play an increasingly important role in visual surveillance applications. Most of the existing approaches in this area, however, have mostly been evaluated without explicitly considering the most relevant gait features, which might have compromised the performance. In this paper, we have investigated the effect of discarding irrelevant or redundant gait features, by employing Genetic Algorithms (GAs) to select an optimal subset of features, on improving the performance of a gait recognition system. Experimental results on the CASIA dataset demonstrate that the proposed system achieves considerable gait recognition performance.