Combining 2D and 3D hand geometry features for biometrie verification

Vivek Kanhangad, Ajay Kumar Pathak, Dapeng Zhang

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

38 Citations (Scopus)

Abstract

Traditional hand geometry based personal verification systems offer limited performance and therefore suitable only for small scale applications. This paper investigates a new approach to achieve performance improvement for hand geometry systems by simultaneously acquiring three dimensional features from the presented hands. The proposed system utilizes a laser based 3D digitizer to acquire registered intensity and range images of the presented hands in a completely contact-free manner, without using any hand position restricting mechanism. Two new representations that characterize the local features on the finger surface are extracted from the acquired range images and are matched using the proposed matching metrics. The proposed approach is evaluated on a database of 177 users, with 10 hand images for each user acquired in two sessions. Our experimental results suggest that the proposed 3D hand geometry features have significant discriminatory information to reliably authenticate individuals. Our experimental results also demonstrate that the combination of 3D hand geometry features with 2D geometry features can be employed to significantly improve the performance from 2D hand geometry features alone.
Original languageEnglish
Title of host publication2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Pages39-44
Number of pages6
DOIs
Publication statusPublished - 20 Nov 2009
Event2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 - Miami, FL, United States
Duration: 20 Jun 200925 Jun 2009

Conference

Conference2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Country/TerritoryUnited States
CityMiami, FL
Period20/06/0925/06/09

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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