Human hand identification with 3D hand pose variations

Vivek Kanhangad, Ajay Kumar Pathak, Dapeng Zhang

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

13 Citations (Scopus)

Abstract

Several recent research efforts in the hand biometrics have focused on developing contact-free personal identification system. However, the acquisition of images from the unconstrained hands can introduce significant hand pose variations which severely limits the performance and applicability of these approaches. This paper presents a new approach to achieve significantly improved performance even in the presence of large hand pose variations. The proposed approach firstly estimates the orientation of the hands in 3D space and then attempts to normalize the pose of the simultaneously acquired 3D and 2D hand images. A new feature representation, namely SurfaceCode, is proposed for matching a pair of 3D palms. Multimodal (2D as well as 3D) palmprint and hand geometry features, which are simultaneously extracted from the texture details of the normalized 3D hands, are used for the matching. Individual matching scores are then consolidated using the weighted sum rule. Our experiments on the database of 114 subjects, with significant pose variations, achieve consistent performance improvement, both for palmprint and hand geometry features considered in this work.
Original languageEnglish
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
Pages17-21
Number of pages5
DOIs
Publication statusPublished - 17 Sep 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 - San Francisco, CA, United States
Duration: 13 Jun 201018 Jun 2010

Conference

Conference2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
CountryUnited States
CitySan Francisco, CA
Period13/06/1018/06/10

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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