Face Verification Based on Bagging RBF Networks

  • Yunhong Wang
  • Yiding Wang
  • Anil K. Jain
  • Tieniu Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)


Face verification is useful in a variety of applications. A face verification system is vulnerable not only to variations in ambient lighting, facial expression and facial pose, but also to the effect of small sample size during the training phase. In this paper, we propose an approach to face verification based on Radial Basis Function (RBF) networks and bagging. The technique seeks to offset the effect of using a small sample size during the training phase. The RBF networks are trained using all available positive samples of a subject and a few randomly selected negative samples. Bagging is then applied to the outputs of these RBF-based classifiers. Theoretical analysis and experimental results show the validity of the proposed approach.


Radial Basis Function Face Image Radial Basis Function Neural Network Radial Basis Function Network Verification System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yunhong Wang
    • 1
  • Yiding Wang
    • 2
  • Anil K. Jain
    • 3
  • Tieniu Tan
    • 4
  1. 1.School of Computer Science and EngineeringBeihang UniversityBeijingChina
  2. 2.Graduate SchoolChinese Academy of SciencesBeijingChina
  3. 3.Department of Computer Science & EngineeringMichigan State UniversityEast Lansing
  4. 4.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingP.R. China

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