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Feature Extraction for Finger-Vein-Based Identity Recognition.

George K Sidiropoulos1, Polixeni Kiratsa1, Petros Chatzipetrou1

  • 1HUMAIN-Lab, Department of Computer Science, International Hellenic University, 654 04 Kavala, Greece.

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|August 30, 2021
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Summary
This summary is machine-generated.

This review examines 13 years of finger vein recognition feature extraction methods. Deep learning, particularly Convolutional Neural Networks, shows growing interest for advanced biometric security.

Keywords:
biometricsdeep learningfeature extractionfinger vein recognitionidentity recognition

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Area of Science:

  • Biometrics and Pattern Recognition
  • Computer Science
  • Artificial Intelligence

Background:

  • Biometric systems are crucial for identity verification.
  • Finger vein recognition offers a unique and secure biometric modality.
  • Feature extraction is key to the performance of biometric systems.

Purpose of the Study:

  • To systematically review feature extraction methods for finger vein recognition.
  • To analyze the evolution of scientific interest in finger vein biometrics from 2008 to 2020.
  • To identify current trends and future research directions in the field.

Main Methods:

  • A systematic literature analysis spanning 13 years (2008-2020).
  • Categorization of feature extraction algorithms into five main groups.
  • Qualitative analysis focusing on techniques for unique identity representation.
  • Examination of deep learning-based non-handcrafted features.

Main Results:

  • Significant and growing scientific interest in finger vein biometric systems.
  • High diversity in proposed feature extraction methods over the years.
  • A recent shift towards Convolutional Neural Networks (CNNs) and deep learning.
  • Identification of limitations in current feature extraction techniques.

Conclusions:

  • Finger vein recognition is a rapidly advancing biometric field.
  • Deep learning approaches, especially CNNs, are becoming dominant.
  • Further research is needed to address the limitations of existing methods and enhance system robustness.