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Correntropy-Induced Discriminative Nonnegative Sparse Coding for Robust Palmprint Recognition.

Kunlei Jing1, Xinman Zhang1, Guokun Song2

  • 1School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, MOE Key Lab for Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)
|August 6, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition. The method enhances security by improving accuracy and flexibility in identifying individuals through palmprints, even with noisy data.

Keywords:
constrained particle swarm optimizercorrentropy metricdiscriminative nonnegative regularizernonnegative constraintregression analysisrobust palmprint recognition

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Area of Science:

  • Biometrics
  • Pattern Recognition
  • Computer Vision

Background:

  • Palmprint recognition is crucial for security applications.
  • Existing methods often lack robustness against data contamination.
  • Need for advanced techniques in robust palmprint identification.

Purpose of the Study:

  • To propose a correntropy-induced discriminative nonnegative sparse coding method for robust palmprint recognition.
  • To enhance the flexibility and robustness of palmprint recognition systems.
  • To extend the method for multispectral palmprint recognition.

Main Methods:

  • Developed a correntropy-induced error estimator combined with the l1-norm for robust feature extraction.
  • Integrated a discriminative nonnegative sparse regularizer for significant feature identification.
  • Employed an analytical optimization approach and a constrained particle swarm optimizer for multispectral fusion.

Main Results:

  • The proposed method demonstrates enhanced flexibility and robustness in palmprint recognition.
  • Effective error detection and correction capabilities were achieved.
  • Successful extension to multispectral palmprint recognition with feature fusion.

Conclusions:

  • The correntropy-induced discriminative nonnegative sparse coding method offers a robust solution for palmprint recognition.
  • The approach is effective in handling various data contaminations.
  • Validated through extensive experiments on multispectral palmprint databases.