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Dynamic biometric identification from multiple views using the GLBP-TOP method.

Yu Wang1, Xuanjing Shen2, Haipeng Chen2

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China Applied Technology College Jilin University, Changchun 130012, China.

Bio-Medical Materials and Engineering
|September 18, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a fast, low-complexity facial recognition method using Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) in the Gabor transform domain. The technique demonstrates robust performance against varying light and expressions for effective dynamic biometric identification.

Keywords:
Biometric identificationLBP-TOPfisher criteriongabor filters

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Area of Science:

  • Computer Vision
  • Biometrics
  • Signal Processing

Background:

  • Dynamic biometric identification requires efficient algorithms with low computational complexity.
  • Existing facial recognition methods face challenges with variations in illumination and facial expressions.

Purpose of the Study:

  • To propose an effective and rapid video-based facial texture program for dynamic biometric identification.
  • To develop a method with low computational complexity for enhanced facial recognition.

Main Methods:

  • Utilized Gabor wavelet transform to obtain enhanced Gabor magnitude maps from normalized faces.
  • Applied the Local Binary Patterns from Three Orthogonal Planes (LBP-TOP) operator to extract video texture.
  • Employed weighted Chi-square statistics based on the Fisher Criterion for identification.

Main Results:

  • The proposed algorithm, Gabor-based LBP-TOP (GLBP-TOP), proved effective in biometric experiments.
  • Demonstrated robustness against significant changes in illumination and facial expressions.
  • Achieved effective and rapid dynamic biometric identification with low computational complexity.

Conclusions:

  • The GLBP-TOP algorithm offers a promising solution for dynamic facial recognition.
  • The method's robustness makes it suitable for real-world biometric applications.
  • Low computational complexity ensures efficient and rapid identification processes.