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Palm-vein classification based on principal orientation features.

Yujia Zhou1, Yaqin Liu2, Qianjin Feng1

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Palm-vein recognition offers secure identification. This study introduces a principal direction feature classification to significantly reduce response times in large palm-vein databases, improving efficiency and accuracy.

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Area of Science:

  • Biometrics and Human-Computer Interaction
  • Pattern Recognition and Machine Learning

Background:

  • Palm-vein pattern recognition is a secure biometric method due to its uniqueness, stability, and resistance to spoofing.
  • Increasing applications of palm-vein recognition lead to larger databases, causing significant response times during identification.
  • Existing methods struggle with efficiency when dealing with extensive palm-vein datasets.

Purpose of the Study:

  • To develop a novel classification method for palm-vein identification to reduce response times.
  • To enhance the efficiency of palm-vein recognition systems, particularly for large-scale databases.
  • To maintain high recognition accuracy while improving processing speed.

Main Methods:

  • Utilized Gaussian-Radon transform to extract orientation matrices and compute principal direction features from palm-vein images.
  • Classified the palm-vein database into six bins based on principal direction values for efficient searching.
  • Implemented a search strategy that includes the target bin and two neighboring bins to optimize identification.

Main Results:

  • Reduced the search range for test samples to approximately 14.29% across multiple databases (PolyU, CASIA, and a custom database).
  • Achieved high retrieval accuracy rates of 96.67% (PolyU), 96.00% (CASIA), and 97.71% (custom database).
  • Decreased identification execution time from 18.56s to 3.16s for a database with 10,000 training samples compared to traditional methods.

Conclusions:

  • The proposed principal direction feature-based classification significantly enhances the efficiency of palm-vein identification systems.
  • The method demonstrates superior performance over traditional approaches, especially with large-scale biometric databases.
  • This approach offers a practical solution for faster and more accurate personal recognition using palm-vein biometrics.