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Gender Identification Using Feature Patch-Based Bayesian Classifier

  • Shen-Ju Lin
  • Chung-Lin Huang
  • Shih-Chung Hsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7088)

Abstract

In the paper, we propose a Bayesian classifier which exploits non-parametric model to identify the gender from the facial images. Our major contribution is that we use feature patch-based non-parametric method to generate the posteriori of male and female based on the characteristics of the labeled training image patches. Our system consists of four modules. First, we use AAM model to identify facial feature points. Facial images are represented by the overlapping feature patches around the feature points. Second, from the labeled training patches, we select a smaller subset as the patch library based on the K means clustering. Third, in training, we embed the gender characteristics of the training feature patches as the posteriori of the library patches. Fourth, in testing, we integrate the posterior of the test patches to determine the gender. The experimental results demonstrate that our proposed method is better than the conventional non-feature-patch-based methods.

Keywords

Gender identification Active Appearance Model Patches-based Bayesian estimation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shen-Ju Lin
    • 1
  • Chung-Lin Huang
    • 1
  • Shih-Chung Hsu
    • 1
  1. 1.Department of Electrical EngineeringNational Tsing-Hua UniversityHsin-ChuR.O.C.

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