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A novel encoding scheme for effective biometric discretization: Linearly Separable Subcode.

Meng-Hui Lim1, Andrew Beng Jin Teoh

  • 1School of Electrical and Electronic Engineering, College of Engineering, Yonsei University, Seoul 120-749, Korea. menghui.lim@ gmail.com

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 30, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces new encoding schemes, Linearly Separable Subcode (LSSC) and Partially Linearly Separable Subcode (PLSSC), to improve biometric discretization. These methods enhance classification performance and output entropy compared to Binary Reflected Gray Code (BRGC).

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Area of Science:

  • Computer Science
  • Information Theory
  • Biometrics

Background:

  • Separability in coding ensures Hamming-distance separation for codewords.
  • In multibit biometric discretization, code separability preserves distance dissimilarity during discrete-to-binary mapping.
  • Binary Reflected Gray Code (BRGC) exhibits inadequacy in handling interclass variation, causing a performance-entropy tradeoff.

Purpose of the Study:

  • To examine the separability of BRGC in biometric discretization.
  • To propose novel encoding schemes that overcome the limitations of BRGC.
  • To improve classification performance and output entropy in biometric systems.

Main Methods:

  • Analysis of Binary Reflected Gray Code (BRGC) separability.
  • Development of two new encoding schemes: Linearly Separable Subcode (LSSC) and Partially Linearly Separable Subcode (PLSSC).
  • Experimental evaluation of the proposed schemes against existing methods for discretization performance.

Main Results:

  • BRGC encoding demonstrates inadequacy in discrete-to-binary mapping for biometrics, creating a performance-entropy tradeoff.
  • LSSC and PLSSC schemes offer full-ideal and near-ideal separability, respectively.
  • Experimental results show the superiority of LSSC and PLSSC over existing schemes in discretization performance.

Conclusions:

  • The proposed LSSC and PLSSC encoding schemes effectively address the limitations of BRGC in biometric discretization.
  • These novel schemes convert the entropy-performance tradeoff into an entropy-redundancy tradeoff, enabling higher classification performance with increased output entropy.
  • The findings open avenues for achieving significantly greater classification accuracy in biometric systems while maintaining high data entropy.