Efficient fingercode classification

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

1 Scopus Citations
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Author(s)

  • Hong-Wei SUN
  • Kwok-Yan LAM
  • Dieter GOLLMANN
  • Siu-Leung CHUNG
  • Jian-Bin LI
  • And 1 others
  • Jia-Guang SUN

Detail(s)

Original languageEnglish
Pages (from-to)1252-1260
Journal / PublicationIEICE Transactions on Information and Systems
VolumeE91-D
Issue number5
Publication statusPublished - May 2008
Externally publishedYes

Abstract

In this paper, we present an efficient fingerprint classification algorithm which is an essential component in many critical security application systems e.g. systems in the e-government and e-finance domains. Fingerprint identification is one of the most important security requirements in homeland security systems such as personnel screening and anti-money laundering. The problem of fingerprint identification involves searching (matching) the fingerprint of a person against each of the fingerprints of all registered persons. To enhance performance and reliability, a common approach is to reduce the search space by firstly classifying the fingerprints and then performing the search in the respective class. Jain et al. proposed a fingerprint classification algorithm based on a two-stage classifier, which uses a K-nearest neighbor classifier in its first stage. The fingerprint classification algorithm is based on the fingercode representation which is an encoding of fingerprints that has been demonstrated to be an effective fingerprint biometric scheme because of its ability to capture both local and global details in a fingerprint image. We enhance this approach by improving the efficiency of the K-nearest neighbor classifier for fingercode-based fingerprint classification. Our research firstly investigates the various fast search algorithms in vector quantization (VQ) and the potential application in fingerprint classification, and then proposes two efficient algorithms based on the pyramid-based search algorithms in VQ. Experimental results on DB1 of FVC 2004 demonstrate that our algorithms can outperform the full search algorithm and the original pyramid-based search algorithms in terms of computational efficiency without sacrificing accuracy.

Research Area(s)

  • Fingercode, Fingerprint classification, Homeland security, System software, Vector quantization

Citation Format(s)

Efficient fingercode classification. / SUN, Hong-Wei; LAM , Kwok-Yan; GOLLMANN, Dieter; CHUNG, Siu-Leung; LI, Jian-Bin; SUN, Jia-Guang.

In: IEICE Transactions on Information and Systems, Vol. E91-D, No. 5, 05.2008, p. 1252-1260.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal