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- W201017002 abstract "We describe an approach of human identification and gender classification based on boxing action. A period detection approach based on time-involved-cutting-plane is first applied and then a boxing sequence of a period is represented by an averaged silhouette. A Nearest Neighbor classifier based on Euclidian distance is used for human identification. The experiments were carried out on the KTH boxing dataset on which the accuracy can reach 80% or higher. After dimensionality reduction by PCA, a SVM is used for gender classification. The experimental results on a dataset containing 20 males and 20 females demonstrate that by applying the proposed algorithm the gender recognition can reach the accuracy of 80% or higher. We also present a numerical analysis of the contributions of different human components. Experimental results show that the head has a positive impact on system performance with the basis of the arm while the buttocks and the leg have not." @default.
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- W201017002 date "2011-01-01" @default.
- W201017002 modified "2023-09-22" @default.
- W201017002 title "Human Identification and Gender Recognition from Boxing" @default.
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- W201017002 doi "https://doi.org/10.1007/978-3-642-25449-9_25" @default.
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