Partial Fingerprint Matching via Phase-Only Correlation and Deep Convolutional Neural Network

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10639)


A major approach for fingerprint matching today is based on minutiae. However, due to the lack of minutiae, their accuracy degrades significantly for partial-to-partial matching. We propose a novel matching algorithm that makes full use of the distinguishing information in partial fingerprint images. Our model employs the Phase-Only Correlation (POC) function to coarsely assign two fingerprints. Then we use a deep convolutional neural network (CNN) with spatial pyramid pooling to measure the similarity of the overlap areas. Experiments indicate that our algorithm has an excellent performance.


Partial fingerprint matching Phase-only correlation Polar Fourier transform Deep convolutional neural networks 



This work was funded by the Chinese National Natural Science Foundation (11331012, 11571014, 11731013).


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Jin Qin
    • 1
  • Siqi Tang
    • 1
  • Congying Han
    • 1
    • 2
    • 3
  • Tiande Guo
    • 1
    • 2
    • 3
  1. 1.University of Chinese Academy of SciencesBeijingChina
  2. 2.School of Mathematical ScienceUCASBeijingChina
  3. 3.Key Laboratory of Big Data Mining and Knowledge ManagementUCASBeijingChina

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