Are Deep Learning Methods Ready for Prime Time in Fingerprints Minutiae Extraction?

  • Ana RebeloEmail author
  • Tiago Oliveira
  • Manual E. Correia
  • Jaime S. Cardoso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


Currently the breakthroughs in most computer vision problems have been achieved by applying deep learning methods. The traditional methodologies that used to successfully discriminate the data features appear to be overwhelmed by the capabilities of learning of the deep network architectures. Nevertheless, many recent works choose to integrate the old handcrafted features into the deep convolutional networks to increase even more their impressive performance. In fingerprint recognition, the minutiae are specific points used to identify individuals and their extraction is a crucial module in a fingerprint recognition system. This can only be emphasized by the fact that the US Federal Bureau of Investigation (FBI) sets as a threshold for a positive identification a number of 8 common minutiae. Deep neural networks have been used to learn possible representations of fingerprint minutiae but, however surprisingly, in this paper it is shown that for now the best choice for an automatic minutiae extraction system is still the traditional road map. A comparison study was conducted with state-of-the-art methods and the best results were achieved by handcraft features.


Biometrics Handcraft features Deep learning Convolutional neural networks Fingerprint verification Image processing 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.INESC TEC Science and TechnologyPortoPortugal

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