Evolutionary-based Feature Selection for Clothing-invariant Gait Recognition

Abstract

Gait recognition, identifying a person by the way they walk, is an unobtrusive way of performing human recognition. The task can be complicated by the presence of covariate factors such as clothing, carrying condition, walking surface, elapsed time, etc. Amongst these factors, clothing is the most challenging one, as it may cover a significant amount of gait features and make human recognition difficult. Since the location of occlusions may differ for different clothing types, relevant gait features may become irrelevant when clothing-type changes, and the use of occluded gait features can hinder the recognition performance. To address this problem, we present a wrapper-based feature selection method that uses evolutionary computation to extract the most relevant and informative gait features for human identification. The proposed method is evaluated using OU-ISIR Treadmill dataset B and the experimental results indicate that our feature selection method significantly improve the performance of gait recognition.