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Feature-Level Fusion of Hand Biometrics for Personal Verification Based on Kernel PCA

  • Qiang Li
  • Zhengding Qiu
  • Dongmei Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3832)

Abstract

This paper presents a novel method of feature-level fusion (FLF) based on kernel principle component analyze (KPCA). The proposed method is applied to fusion of hand biometrics include palmprint, hand shape and knuckleprint, and we name the new feature as “handmetric”. For different kind of samples, polynomial kernel is employed to generate the kernel matrixes that indicate the relationship among them. While fusing these kernel matrixes by fusion operators and extracting principle components, the handmetric feature space is established and nonlinear feature-level fusion projection could be implemented. The experimental results testify that the method is efficient for feature fusion, and could keep more identity information for verification.

Keywords

Kernel Matrix Fusion Algorithm Hand Shape Hand Image Fusion Operator 
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

  • Qiang Li
    • 1
  • Zhengding Qiu
    • 1
  • Dongmei Sun
    • 1
  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingP.R. China

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