Contactless and pose invariant biometric identification using hand surface

Vivek Kanhangad, Ajay Kumar Pathak, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

60 Citations (Scopus)

Abstract

This paper presents a novel approach for hand matching that achieves significantly improved performance even in the presence of large hand pose variations. The proposed method utilizes a 3-D digitizer to simultaneously acquire intensity and range images of the user's hand presented to the system in an arbitrary pose. The approach involves determination of the orientation of the hand in 3-D space followed by pose normalization of the acquired 3-D and 2-D hand images. Multimodal (2-D as well as 3-D) palmprint and hand geometry features, which are simultaneously extracted from the user's pose normalized textured 3-D hand, are used for matching. Individual matching scores are then combined using a new dynamic fusion strategy. Our experimental results on the database of 114 subjects with significant pose variations yielded encouraging results. Consistent (across various hand features considered) performance improvement achieved with the pose correction demonstrates the usefulness of the proposed approach for hand based biometric systems with unconstrained and contact-free imaging. The experimental results also suggest that the dynamic fusion approach employed in this work helps to achieve performance improvement of 60% (in terms of EER) over the case when matching scores are combined using the weighted sum rule.
Original languageEnglish
Article number5620980
Pages (from-to)1415-1424
Number of pages10
JournalIEEE Transactions on Image Processing
Volume20
Issue number5
DOIs
Publication statusPublished - 1 May 2011

Keywords

  • 3-D hand geometry
  • 3-D Palmprint
  • Contactless palmprint
  • dynamic Fusion
  • hand biometrics
  • SurfaceCodes

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

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