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. © 2011 Springer-Verlag.
CITATION STYLE
Wang, J., Hu, W., Wang, Z., & Chen, Z. (2011). Human identification and gender recognition from boxing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7098 LNCS, pp. 195–203). https://doi.org/10.1007/978-3-642-25449-9_25
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