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The analysis for Gait Energy Image based on statistical methods

Gait features representation is an important step in gait biometric recognition. It represents content of the individual image and the pattern of human walking simultaneously. Discriminative features can provide the most relevant and useful information for individual classification. Many state of the art feature fusions based on average silhouette were used for gait feature representation technique. Those fusion methods are mostly robust in simple case but inadequate for difficult gait representation such as walking with occlusion, walking with different view and walking with briefcase that affect the accuracy of gait performance. For that reason, we are exploring and proposing another potential feature fusion based on statistical method namely edges directed matrix of Gait Energy Image (EDMSGEI). We compare our EDMSGEI method with Gray Level Co-Occurrence Matrix (GLCMGEI) and Histogram of Gaussian (HOGGEI) method for individual recognition with K-Nearest neighbour classifier. The datasets involved are CASIA and UKM-CSM dataset images. Apart from HOGGEI, this study also shows that EDMSGEI gained better gait recognition performance.