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Novel Local Coding Algorithm for Finger Multimodal Feature Description and Recognition.

Shuyi Li1, Haigang Zhang2, Yihua Shi3

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Summary
This summary is machine-generated.

This study introduces a new method for finger-based biometrics, combining fingerprint, finger-vein, and finger-knuckle-print traits. The approach enhances accuracy and efficiency in personal identification systems.

Keywords:
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Area of Science:

  • Biometrics
  • Computer Vision
  • Pattern Recognition

Background:

  • Finger-based biometrics (fingerprint, finger-vein, finger-knuckle-print) are convenient for personal identification.
  • Feature expression insensitive to illumination and pose variations is crucial for improving biometric recognition.
  • Developing reliable feature description methods is significant for advancing finger-based biometric systems.

Purpose of the Study:

  • To propose a pose correction approach for finger trimodal images.
  • To introduce a novel local coding-based feature expression method for fusing fingerprint, finger-vein, and finger-knuckle-print traits.
  • To enhance the performance of finger-based biometric recognition systems.

Main Methods:

  • A pose correction approach was developed to address inconsistency in finger trimodal images.
  • A local coding-based feature expression method was introduced, utilizing oriented Gabor filters for direction feature enhancement.
  • A generalized symmetric local graph structure (GSLGS) was developed to capture neighborhood pixel relationships.

Main Results:

  • The proposed method demonstrated excellent performance in improving matching accuracy.
  • The approach showed significant improvements in recognition efficiency.
  • Experimental results validated the effectiveness of the novel coding-based feature expression.

Conclusions:

  • The developed pose correction and feature fusion method effectively enhances finger-based biometric recognition.
  • The local coding-based approach offers a reliable way to describe and fuse multi-modal finger traits.
  • This research contributes to the development of more robust and efficient personal identification systems.