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Multi-Order Extension Codes for Palmprint Recognition.

Fengxiang Liao1, Lu Leng1, Ziyuan Yang2

  • 1Jiangxi Province Key Laboratory of Image Processing and Pattern Recognition, Nanchang Hangkong University, 696 Fenghe Nan Avenue, Nanchang, 330063 Jiangxi, P. R. China.

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|July 7, 2025
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Summary
This summary is machine-generated.

This study introduces multi-order extensions for palmprint recognition, incorporating both first-order and second-order texture features (1TFs and 2TFs). This novel approach significantly enhances the accuracy of palmprint texture coding methods for improved biometric identification.

Keywords:
Biometric recognitionmulti-order extensionpalmprint recognitionsecond-order texture features

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Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Vision
  • Image Processing

Background:

  • Palmprint recognition is a key biometric modality with many applications.
  • Current methods primarily use first-order texture features (1TFs), overlooking valuable second-order texture features (2TFs).
  • Gabor filters are effective texture extractors inspired by the nervous system.

Purpose of the Study:

  • To propose multi-order extensions for existing palmprint texture coding methods.
  • To leverage both first-order texture features (1TFs) and second-order texture features (2TFs) for improved recognition.
  • To establish a general framework for texture-based recognition tasks.

Main Methods:

  • A multi-order extension framework is proposed for palmprint texture coding.
  • First-order texture features (1TFs) are extracted using a filter.
  • Second-order texture features (2TFs) are extracted from the 1TFs using the same filter, with variations in filters for diverse textures.

Main Results:

  • The multi-order extension framework effectively utilizes both 1TFs and 2TFs.
  • Simultaneous participation of 1TFs and 2TFs in codes leads to more discriminative feature extraction and fusion.
  • Experimental results demonstrate remarkable accuracy improvements across three public databases (contact, noncontact, multispectral).

Conclusions:

  • Multi-order extension significantly enhances the accuracy of palmprint texture coding methods.
  • The proposed framework offers a generalizable approach applicable to other texture-based recognition tasks.
  • This method provides a more robust and discriminative feature set for biometric identification.