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- W2572224462 abstract "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." @default.
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- W2572224462 date "2016-08-01" @default.
- W2572224462 modified "2023-09-26" @default.
- W2572224462 title "Combination of gait multiple features at matching score level" @default.
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- W2572224462 doi "https://doi.org/10.1109/iced.2016.7804688" @default.
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