Iris recognition using Quaternionic Sparse Orientation Code (QSOC)

Ajay Kumar Pathak, Tak Shing Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

20 Citations (Scopus)

Abstract

Personal identification from the iris images acquired under less-constrained imaging environment is highly challenging problem but with several important applications in surveillance, image forensics, search for missing children and wandering elderly. In this paper, we develop and formulate a new approach for the iris recognition using hypercomplex (quaternionic or octonionic) and sparse representation of unwrapped iris images. We model iris representation problem as quaternionic sparse coding problem which is solved by convex optimization strategy. This approach essentially exploits the orientation of local iris texture elements which are efficiently extracted using a binarized dictionary of oriented atoms. The feasibility of this approach is evaluated, both for the recognition and the verification problem, on the publicly available visible illumination UBIRIS V2 database. Our experimental results using the proposed formulation illustrate significant improvement in performance (e.g., ∼30% improvement in rank-one recognition accuracy) over the previously studied sparse representation approach for the visible illumination iris recognition.
Original languageEnglish
Title of host publication2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012
Pages59-64
Number of pages6
DOIs
Publication statusPublished - 20 Aug 2012
Event2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012 - Providence, RI, United States
Duration: 16 Jun 201221 Jun 2012

Conference

Conference2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2012
Country/TerritoryUnited States
CityProvidence, RI
Period16/06/1221/06/12

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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