Reconstruction based finger-knuckle-print verification with score level adaptive binary fusion

Guangwei Gao, Lei Zhang, Jian Yang, Lin Zhang, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

36 Citations (Scopus)

Abstract

Recently, a new biometrics identifier, namely finger knuckle print (FKP), has been proposed for personal authentication with very interesting results. One of the advantages of FKP verification lies in its user friendliness in data collection. However, the user flexibility in positioning fingers also leads to a certain degree of pose variations in the collected query FKP images. The widely used Gabor filtering based competitive coding scheme is sensitive to such variations, resulting in many false rejections. We propose to alleviate this problem by reconstructing the query sample with a dictionary learned from the template samples in the gallery set. The reconstructed FKP image can reduce much the enlarged matching distance caused by finger pose variations; however, both the intra-class and inter-class distances will be reduced. We then propose a score level adaptive binary fusion rule to adaptively fuse the matching distances before and after reconstruction, aiming to reduce the false rejections without increasing much the false acceptances. Experimental results on the benchmark PolyU FKP database show that the proposed method significantly improves the FKP verification accuracy.
Original languageEnglish
Article number6595604
Pages (from-to)5050-5062
Number of pages13
JournalIEEE Transactions on Image Processing
Volume22
Issue number12
DOIs
Publication statusPublished - 21 Oct 2013

Keywords

  • Biometrics
  • Finger-knuckle-print
  • Reconstruction
  • Score level fusion

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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