A computationally efficient approach to 3D ear recognition employing local and holistic features

Jindan Zhou, Steven Cadavid, Mohamed Abdel-Mottaleb

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

We present a complete, Three-Dimensional (3D) object recognition system combining local and holistic features in a computationally efficient manner. An evaluation of the proposed system is conducted on a 3D ear recognition task. The ear provides a challenging case study because of its high degree of inter-subject similarity. In this work, we focus primarily on the local and holistic feature extraction and matching components, as well as the fusion framework used to combine these features at the match score level. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing range images of 415 subjects, yielded a rank-one recognition rate of 98.6% and an equal error rate of 1.6%. These results demonstrate that the proposed system outperforms state-of-the-art 3D ear biometric systems.

Original languageEnglish (US)
Title of host publication2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011
DOIs
StatePublished - Oct 31 2011
Event2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011 - Colorado Springs, CO, United States
Duration: Jun 20 2011Jun 25 2011

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Other

Other2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011
CountryUnited States
CityColorado Springs, CO
Period6/20/116/25/11

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Zhou, J., Cadavid, S., & Abdel-Mottaleb, M. (2011). A computationally efficient approach to 3D ear recognition employing local and holistic features. In 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011 [5981815] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). https://doi.org/10.1109/CVPRW.2011.5981815