Human identification and gender recognition from boxing

  • Authors:

  • Jian Wang;Wuzhenni Hu;Zhiling Wang;Zonghai Chen

  • Affiliations:

  • Department of Automation, University of Science and Technology of China, China;Department of Computer Science, University of Science and Technology of China, China;Department of Automation, University of Science and Technology of China, China;Department of Automation, University of Science and Technology of China, China

  • Venue:
  • CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
  • Year:
  • 2011

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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.