Gender Classification by Combining Facial and Hair Information

  • Xiao-Chen Lian
  • Bao-Liang Lu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5507)


Most of the existing gender classification approaches are based on face appearance only. In this paper, we present a gender classification system that integrates face and hair features. Instead of using the whole face we extract features from eyes, nose and mouth regions with Maximum Margin Criterion (MMC), and the hair feature is represented by a fragment-based encoding. We use Support Vector Machines with probabilistic output (SVM-PO) as individual classifiers. Fuzzy integration based classifier combination mechanism is used to fusing the four different classifiers on eyes, nose, mouth and hair respectively. The experimental results show that the MMC outperforms Principal Component Analysis and Fisher Discriminant Analysis and incorporating hair feature improves gender classification performance.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xiao-Chen Lian
    • 1
  • Bao-Liang Lu
    • 1
  1. 1.Department of Computer Science and Engineering MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent SystemsShanghai Jiao Tong UniversityShanghaiChina

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