This paper focus to analyze several fusion rule at matching score level to combine important features extracted from gait sequence images for human identification system. Gait sequence image is a non-stationary data and can be modelled using a statistical learning technique. The propose technique consists of three different stages. The pre-processing stage computes the average silhouette images to capture the important information and get a better representation for gait silhouette data. Then a principle component analysis (PCA) technique is applied on the average silhouette to extract the important gait features and reduce a dimension of gait data. Three different features are fused at matching score level by using sum, product and max rule. The proposed algorithm has been tested using a benchmark CASIA datasets. The experimental results show that the best recognition rate is 90% when the fusion is performed using sum rule.
