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Hybrid Feature Extractor Using Discrete Wavelet Transform and Histogram of Oriented Gradient on

Meirista Wulandari1, Rifai Chai2, Basari Basari1,3

  • 1Department of Electrical Engineering, Universitas Indonesia, Depok 16424, Jawa Barat, Indonesia.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces VeinCNN, a novel palm vein recognition method using hybrid feature extraction. VeinCNN achieves high accuracy and reliability for biometric security systems, demonstrating superior performance on public datasets.

Keywords:
CNNDWTHOGVeinCNNpalm vein

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Area of Science:

  • Computer Science
  • Biometrics
  • Image Processing

Background:

  • Biometric recognition is crucial for security and attendance.
  • Palm vein biometrics offer enhanced security due to their intrinsic nature.
  • Infrared palm vein images present challenges like nonuniform illumination and low contrast.

Purpose of the Study:

  • To develop an accurate and reliable palm vein recognition method.
  • To address the limitations of low contrast and nonuniform illumination in palm vein images.
  • To evaluate the proposed method's performance using key biometric metrics.

Main Methods:

  • A convolutional neural network (CNN) model named VeinCNN was developed.
  • Hybrid feature extraction using Discrete Wavelet Transform (DWT) and Histogram of Oriented Gradient (HOG) was employed.
  • The method was tested on five public datasets: CASIA, Vera, Tongji, PolyU, and PUT.

Main Results:

  • The VeinCNN method demonstrated promising results in accuracy, Area Under the Curve (AUC), and Equal Error Rate (EER).
  • The highest performance was achieved on the CASIA dataset, with 99.85% accuracy, 99.80% AUC, and 0.0083 EER.
  • The hybrid feature extraction approach proved effective in overcoming image quality issues.

Conclusions:

  • The proposed VeinCNN recognition scheme offers a robust solution for palm vein-based biometric verification.
  • The hybrid DWT and HOG feature extraction effectively enhances recognition accuracy.
  • VeinCNN shows significant potential for real-world applications in secure identification systems.