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Partial Palmprint Matching Using Invariant Local Minutiae Descriptors

  • Moussadek Laadjel
  • Ahmed Bouridane
  • Fatih Kurugollu
  • Omar Nibouche
  • WeiQi Yan
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6010)

Abstract

In forensic investigations, it is common for forensic investigators to obtain a photograph of evidence left at the scene of crimes to aid them catch the culprit(s). Although, fingerprints are the most popular evidence that can be used, scene of crime officers claim that more than 30% of the evidence recovered from crime scenes originate from palms. Usually, palmprints evidence left at crime scenes are partial since very rarely full palmprints are obtained. In particular, partial palmprints do not exhibit a structured shape and often do not contain a reference point that can be used for their alignment to achieve efficient matching. This makes conventional matching methods based on alignment and minutiae pairing, as used in fingerprint recognition, to fail in partial palmprint recognition problems. In this paper a new partial-to-full palmprint recognition based on invariant minutiae descriptors is proposed where the partial palmprint’s minutiae are extracted and considered as the distinctive and discriminating features for each palmprint image. This is achieved by assigning to each minutiae a feature descriptor formed using the values of all the orientation histograms of the minutiae at hand. This allows for the descriptors to be rotation invariant and as such do not require any image alignment at the matching stage. The results obtained show that the proposed technique yields a recognition rate of 99.2%. The solution does give a high confidence to the judicial jury in their deliberations and decision.

Keywords

Minutiae Descriptor Orientation Histogram Partial Palmprint 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Moussadek Laadjel
    • 1
  • Ahmed Bouridane
    • 2
    • 3
  • Fatih Kurugollu
    • 1
  • Omar Nibouche
    • 1
  • WeiQi Yan
    • 1
  1. 1.The Institute of Electronics, Communications and Information TechnologyQueen’s University BelfastBelfastUnited Kingdom
  2. 2.School of Computing, Engineering and Information SciencesNorthumbria UniversityNewcastle upon TyneUnited Kingdom
  3. 3.College of Computer and Information SciencesKing Saud UniversityRiyadhKingdom of Saudi Arabia

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