Robust gait recognition based on partitioning and canonical correlation analysis

Gait recognition would be greatly affected by some covariate factors including clothing type and carrying objects. Finding an approach robust to these covariate factors is the most challenging problem. In this paper, we propose a method based on canonical correlation analysis (CCA) to model the correlation between gait sequences under two different walking conditions. Correlation strength is used in KNN classifier as similarity measure. GEIs are partitioned into several parts and vast majority voting is employed among these parts to reduce the effect of the covariate factors. Experiment results show that our proposed method outperforms other classical methods over all views.