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SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy.

Suhang Chen1, Sheng Chang1, Qijun Huang1

  • 1School of Physics and Technology, Wuhan University, Wuhan, China.

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Summary
This summary is machine-generated.

This study introduces a novel algorithm to detect fake fingerprints, achieving over 98% accuracy in distinguishing synthetic from real prints for automated fingerprint identification systems (AFISs).

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Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Synthetic fingerprints pose a significant security risk to Automatic Fingerprint Identification Systems (AFISs).
  • Developing robust methods to differentiate between real and artificial fingerprints is crucial for maintaining system integrity.
  • Existing systems may be vulnerable to sophisticated synthetic fingerprint attacks.

Purpose of the Study:

  • To propose and validate an effective algorithm for discriminating synthetic fingerprints from genuine ones.
  • To enhance the security and reliability of AFISs against spoofing attempts.
  • To introduce a quantitative optimization strategy for synthetic fingerprint detection.

Main Methods:

  • Extraction of four key characteristic factors: ridge distance, global gray, frequency, and Harris Corner features.
  • Utilizing a Support Vector Machine (SVM) classifier for binary classification of fingerprints.
  • Development of a novel performance factor to evaluate SVM accuracy and efficiency.
  • Establishment of a quantitative optimization strategy for SVM parameters.

Main Results:

  • The proposed algorithm achieved a recognition accuracy rate exceeding 98% on discrete and mixed synthetic fingerprint databases.
  • The study identified optimal SVM kernel types and training sample proportions based on desired accuracy levels.
  • For 95% accuracy, a polynomial kernel with 5% training samples was optimal.
  • For 98% accuracy, an RBF kernel with 15% training samples was more suitable.

Conclusions:

  • The developed algorithm offers a highly accurate and efficient solution for synthetic fingerprint detection.
  • The quantitative optimization strategy provides a framework for tuning biometric security systems.
  • This research significantly advances the capability to secure AFISs against synthetic fingerprint threats.