Real-time Gender Classification from Human Gait for Arbitrary View Angles

  • Authors:
  • Ping-Chieh Chang;Ming-Chun Tien;Ja-Ling Wu;Chuan-Shen Hu

  • Affiliations:
  • -;-;-;-

  • Venue:
  • ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
  • Year:
  • 2009

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Abstract

In this paper, we investigate an important but understudied problem, gender classification from human gaits. And we have proved the ability of using GEI (Gait Energy Image) as a representation of human gait for arbitrary view angles. Using GEI as a discriminative feature, we construct angle classifiers and gender classifiers from different approaches. Experiments show that our system achieved a good performance in real-time and is able to be applied to real-world application.