Improvement of 16% in test error when compared with the best Our best model achievesĪn overall rate of 97.1% of correctly classified samples - a relative Report good accuracy on very small training sets (400 samples)
Only for deep architectures but also for shallow ones.
DatasetĪugmentation is used to increase the classifiers performance, not No need for architecture or hyperparameter selection. That pre-trained CNNs can yield state-of-the-art results with Weights, and a classical Local Binary Pattern approach. We compareįour different models: two CNNs pre-trained on natural imagesĪnd fine-tuned with the fingerprint images, CCN with random System is evaluated on the datasets used in The Liveness DetectionĬompetition of years 2009, 20, which compriseĪlmost 50,000 real and fake fingerprints images. Neural Networks (CNN) for fingerprint liveness detection. Systems in the recent years, spoof fingerprint detection has become With the growing use of biometric authentication We also present a graphical user interface, called Fingerprint Spoof Buster, that allows the operator to visually examine the local regions of the fingerprint highlighted as live or spoof, instead of relying on only a single score as output by the traditional approaches. Additionally, two new fingerprint presentation attack datasets containing more than 20,000 images, using two different fingerprint readers, and over 12 different spoof fabrication materials are collected. For example, in LivDet 2015, the proposed approach achieves 99.03% average accuracy over all sensors compared to 95.51% achieved by the LivDet 2015 competition winners. Experimental results on three public-domain LivDet datasets (2011, 2013, and 2015) show that the proposed approach provides state-of-the-art accuracies in fingerprint spoof detection for intra-sensor, crossmaterial, cross-sensor, as well as cross-dataset testing scenarios. Specifically, we propose a deep convolutional neural network based approach utilizing local patches centered and aligned using fingerprint minutiae. This study addresses the problem of developing accurate, generalizable, and efficient algorithms for detecting fingerprint spoof attacks. The primary purpose of a fingerprint recognition system is to ensure a reliable and accurate user authentication, but the security of the recognition system itself can be jeopardized by spoof attacks.
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