A 3D Feature Descriptor Recovered from a Single 2D Palmprint Image

Qian Zheng, Ajay Kumar Pathak, Gang Pan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

41 Citations (Scopus)

Abstract

Design and development of efficient and accurate feature descriptors is critical for the success of many computer vision applications. This paper proposes a new feature descriptor, referred to as DoN, for the 2D palmprint matching. The descriptor is extracted for each point on the palmprint. It is based on the ordinal measure which partially describes the difference of the neighboring points' normal vectors. DoN has at least two advantages: 1) it describes the 3D information, which is expected to be highly stable under commonly occurring illumination variations during contactless imaging; 2) the size of DoN for each point is only one bit, which is computationally simple to extract, easy to match, and efficient to storage. We show that such 3D information can be extracted from a single 2D palmprint image. The analysis for the effectiveness of ordinal measure for palmprint matching is also provided. Four publicly available 2D palmprint databases are used to evaluate the effectiveness of DoN, both for identification and the verification. Our method on all these databases achieves the state-of-the-art performance.
Original languageEnglish
Article number7464743
Pages (from-to)1272-1279
Number of pages8
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume38
Issue number6
DOIs
Publication statusPublished - 1 Jun 2016

Keywords

  • 3D feature from a single 2D image
  • biometrics
  • contactless palmprint matching
  • ordinal features
  • Palmprint recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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