An incremental learning method for spoof fingerprint detection

Jun Beom Kho, Wonjune Lee, Heeseung Choi, Jaihie Kim

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)


Spoof fingerprint detectors based on static features are built by learning a set of live and fake fingerprint images. These learning-based spoof detectors cannot accurately classify new or untrained types of fakes. To handle this problem, the existing spoof detector should be incrementally trained on the new types of fakes. This paper proposes a new spoof detection framework to learn new types of fakes incrementally without retraining the existing spoof detector repeatedly. The proposed model discriminates the newly learned fakes without serious loss of performance for the previously learned fakes and at the same time provides promising detection results for the various types of fakes. The proposed spoof detector integrates multiple “experts,” each of which shares the same structure but is separately trained for a different set of fake fingerprints. To detect a new type of fake fingerprint, a new expert exclusively trained on the new fake type is integrated into the spoof detector. Each expert consists of multiple support vector machines (SVMs) applied by an incremental learning algorithm (Learn++.NC), where each SVM adopts one of three texture features for spoof detection. Experimental results show the superiority of the proposed method compared with other methods in various scenarios.

Original languageEnglish
Pages (from-to)52-64
Number of pages13
JournalExpert Systems with Applications
Publication statusPublished - 2019 Feb

Bibliographical note

Funding Information:
The authors would like to thank Dr. Anil Jain of Michigan State University for his support to the work. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (No. 2016R1A2B4006320 ).

Publisher Copyright:
© 2018 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence


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