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  1. Home
  3. Dual-model Synergy For Fingerprint Spoof Detection Using Vgg16 And Resnet50.
  1. Home
  3. Dual-model Synergy For Fingerprint Spoof Detection Using Vgg16 And Resnet50.

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Dual-Model Synergy for Fingerprint Spoof Detection Using VGG16 and ResNet50.

Mohamed Cheniti1, Zahid Akhtar2, Praveen Kumar Chandaliya3

  • 1Faculty of Electrical Engineering, Telecommunications Department, Laboratory (LTIR), University of Science and Technology Houari Boumediene, BP.32, EI-Alia, Bab-Ezzouar, Algiers 16111, Algeria.

Journal of Imaging
|February 25, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a dual pre-trained model using VGG16 and ResNet50 for robust fingerprint liveness detection. The combined approach significantly improves accuracy and reduces error rates compared to single-model methods.

Keywords:
Livedet2013Livedet2015ResNet50VGG16biometric anti-spoofingerror rate analysis (BPCER, APCER)fingerprint liveness detectionspoof attacks

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Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Fingerprint liveness detection is crucial for security.
  • Existing methods using single feature extractors struggle with generalization.
  • Diverse spoofing materials and sensor types pose significant challenges.

Purpose of the Study:

  • To develop an advanced fingerprint liveness detection system.
  • To enhance generalization capabilities across various spoofing attacks.
  • To improve the accuracy and reliability of biometric security systems.

Main Methods:

  • A dual pre-trained model approach combining VGG16 and ResNet50 architectures.
  • Feature concatenation from VGG16 (high-resolution) and ResNet50 (deep layers).
  • Classification of fingerprints as live or spoofed using integrated features.

Main Results:

  • Achieved state-of-the-art accuracy: 99.72% on Livedet2013 and 96.32% on Livedet2015.
  • Demonstrated low error rates: Bonafide Presentation Classification Error Rate (BPCER) of 0.28% (LivDet 2013) and 1.45% (LivDet 2015).
  • Observed higher Attack Presentation Classification Error Rate (APCER) for unknown spoof materials (e.g., 8.12% on Crossmatch subset).

Conclusions:

  • The dual-model framework offers robust and adaptable fingerprint liveness detection.
  • The approach effectively captures comprehensive features for improved spoof detection.
  • Further optimization is needed to address performance on unseen spoof materials.