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Liveness Detection of Anti-spoofing Fingerprints using Machine learning

  • Unique Paper ID: 160169
  • Volume: 9
  • Issue: 12
  • PageNo: 1302-1306
  • Abstract:
  • With the growing use of biometric authentication systems in the recent years, spoof fingerprint detection has become increasingly important. In this study, we use Convolutional Neural Networks (CNN) for fingerprint liveness detection. Our system is evaluated on the datasets used in The Liveness Detection Competition of years 2009, 2011 and 2013, which comprise almost 50,000 real and fake fingerprints images. We compare four different models: two CNNs pre-trained on natural images and fine-tuned with the fingerprint images, CCN with random weights, and a classical Local Binary Pattern approach. We show that pre-trained CNNs can yield state-of-the-art results with no need for architecture or hyperparameter selection. Dataset Augmentation is used to increase the classifiers performance, not only for deep architectures but also for shallow ones. We also report good accuracy on very small training sets (400 samples) using these large pre-trained networks. Our best model achieves an overall rate of 97.1% of correctly classified samples - a relative improvement of 16% in test error when compared with the best previously published results. This model won the first prize in the Fingerprint Liveness Detection Competition (LivDet) 2015 with an overall accuracy of 95.5%.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 12
  • PageNo: 1302-1306

Liveness Detection of Anti-spoofing Fingerprints using Machine learning

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