Alignment-free row-co-occurrence cancelable palmprint Fuzzy Vault

Research output: Contribution to journalArticlepeer-review

68 Citations (Scopus)

Abstract

Texture coding approach of palmprint biometric enjoys several merits; however, palmprint texture code suffers from alignment problem during matching, which hinders it from being directly adopted in biometric cryptosystems, such as Fuzzy Vault. On the other hand, Fuzzy Vault itself is plagued by several security and privacy issues if biometric template is left unprotected. In this paper, we proposed two hybrid cancelable palmprint cryptosystems based on palmprint texture code, dubbed row-alone and row-co-occurrence Fuzzy Vaults. To be specific, we blend 2DPalmHash Code (2DPHC), a cancelable biometric scheme, and Fuzzy Vault primitive to jointly protect palmprint templates. In row-alone Fuzzy Vault, we leverage the horizontal displacement tolerance trait of 2DPHC to extract an alignment-free feature set for polynomial projection. We further generalize row-alone Fuzzy Vault to row-co-occurrence Fuzzy Vault to improve the key recovery rate. Besides that, we demonstrate that 2DPHC-based Fuzzy Vault enhances the robustness against several security attacks.

Original languageEnglish
Pages (from-to)2290-2303
Number of pages14
JournalPattern Recognition
Volume48
Issue number7
DOIs
Publication statusPublished - 2015 Jul 1

Bibliographical note

Funding Information:
The authors would also like to thank Biometric Research Center at Hong Kong Polytechnic University for providing us with the palmprint database. This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by Ministry of Science, ICT and Future Planning ( 2013006574 ), National Natural Science Foundation of China ( 61305010 ) and Institute of BioMed-IT, Energy-IT and Smart-IT Technology (BEST), a Brain Korea 21 Plus Program, Yonsei University.

Publisher Copyright:
© 2015 Elsevier Ltd.

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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