Reduction of Gait Covariate Factors Using Feature Selection and Sparse Dictionary Learning

摘要

Human gait recognition is still a very challenging problem because natural human gait is affected by many covariate conditions such as changes in the clothing, variations in viewing angle, and changes in carrying condition. Although existing gait recognition methods perform well under a controlled environment where the gait is in normal condition with no covariate factors, the performance drastically decreases in practical conditions where it is susceptible to many covariate factors. This paper analyzes 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. We also propose an augmentation technique to overcome some of the problems associated with the intra-class gait fluctuations, as well as when the amount of the training data is relatively small. Finally, we use dictionary learning with sparse coding and Linear Discriminant Analysis (LDA) to seek the best discriminative data representation before feeding it to the Nearest Centroid classifier. When our method is applied on the large CASIAB and OU-ISIR-B gait data sets, it outperforms existing gait methods.