Detection of fingerprint alterations using deep convolutional neural networks

Yahaya Isah Shehu, Ariel Ruiz-Garcia, Vasile Palade, Anne James

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

1 Citation (Scopus)

Abstract

Fingerprint alteration is a challenge that poses enormous security risks. As a result, many research efforts in the scientific community have attempted to address this issue. However, non-existence of publicly available datasets that contain obfuscation and distortion of fingerprints makes it difficult to identify the type of alteration. In this work we present the publicly available Sokoto-Coventry Fingerprints Dataset (SOCOFing), which provides ten fingerprints for 600 different subjects, as well as gender, hand and finger name for each image, among other unique characteristics. We also provide a total of 55,249 images with three levels of alteration for Z-cut, obliteration and central rotation synthetic alterations, which are the most common types of obfuscation and distortion. In addition, this paper proposes a Convolutional Neural Network (CNN) to identify these alterations. The proposed CNN model achieves a classification accuracy rate of 98.55%. Results are also compared with a residual CNN model pre-trained on ImageNet, which produces an accuracy of 99.88%.

Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings
PublisherSpringer-Verlag London Ltd
Pages51-60
Number of pages10
ISBN (Print)9783030014179
DOIs
Publication statusE-pub ahead of print - 27 Sep 2018
Event27th International Conference on Artificial Neural Networks, ICANN 2018 - Rhodes, Greece
Duration: 4 Oct 20187 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11139 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference27th International Conference on Artificial Neural Networks, ICANN 2018
CountryGreece
CityRhodes
Period4/10/187/10/18

    Fingerprint

Keywords

  • Central rotation
  • Convolutional neural networks
  • Distortion
  • Fingerprint alteration
  • Obfuscation
  • Obliteration
  • Z-cut

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Shehu, Y. I., Ruiz-Garcia, A., Palade, V., & James, A. (2018). Detection of fingerprint alterations using deep convolutional neural networks. In Artificial Neural Networks and Machine Learning – ICANN 2018 - 27th International Conference on Artificial Neural Networks, 2018, Proceedings (pp. 51-60). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11139 LNCS). Springer-Verlag London Ltd. https://doi.org/10.1007/978-3-030-01418-6_6