Gender classification using the profile

  • Authors:
  • Wankou Yang;Amrutha Sethuram;Eric Patternson;Karl Ricanek;Changyin Sun

  • Affiliations:
  • Face Aging Group, Dept. Of Computer Science, UNCW and School of Automation, Southeast University, Nanjing, China;Face Aging Group, Dept. Of Computer Science, UNCW;Face Aging Group, Dept. Of Computer Science, UNCW;Face Aging Group, Dept. Of Computer Science, UNCW;School of Automation, Southeast University, Nanjing, China

  • Venue:
  • ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
  • Year:
  • 2011

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Abstract

Gender classification has attracted a lot of attention in computer vision and pattern recognition. In this paper, we propose a gender classification method. First, we present a robust profile extraction algorithm; Second, we implement Principal Components Analysis (PCA) and Independent Components Analysis (ICA) to extract discriminative features from profile to estimate the face gender via SVM. Our experimental results on Bosphorus 3D face database show that our proposed method works well.