Reducing covariate factors of gait recognition using feature selection and dictionary-based sparse coding

Abstract

Human gait recognition is a behavioral biometrics method that aims to determine the identity of individuals through the manner and style of their distinctive walk. It is still a very challenging problem because natural human gait is affected by many covariate factors such as changes in the clothing, variations in viewing angle, and changes in carrying condition. This paper evaluates the most important features of gait under the carrying and clothing conditions. We find that the intra-class variations of the features that remain static during the gait cycle affect the recognition accuracy adversely. Thus, we introduce an effective and robust feature selection method based on the gait energy image. The new gait representation is less sensitive to these covariate factors. We also propose an augmentation technique to overcome some of the problems associated with the intra-class gait fluctuations, as well as if the amount of the training data is relatively small. Finally, we use dictionary learning with sparse coding and linear discriminant analysis to seek the best discriminative data representation before feeding it to the Nearest Centroid classifier. When our method is applied on the large CASIA-B gait data set, we are able to outperform existing gait methods by achieving the highest average result.