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

Jindan Zhou; Steven Cadavid; Mohamed Abdel-Mottaleb

doi:10.1109/CVPRW.2011.5981815

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

Jindan Zhou, Steven Cadavid, Mohamed Abdel-Mottaleb

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

20 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 language	English (US)
Title of host publication	2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011
DOIs	https://doi.org/10.1109/CVPRW.2011.5981815
State	Published - Oct 31 2011
Event	2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011 - Colorado Springs, CO, United States Duration: Jun 20 2011 → Jun 25 2011

Publication series

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

Other

Other	2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011
Country	United States
City	Colorado Springs, CO
Period	6/20/11 → 6/25/11

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Access to Document

10.1109/CVPRW.2011.5981815

Fingerprint Dive into the research topics of 'A computationally efficient approach to 3D ear recognition employing local and holistic features'. Together they form a unique fingerprint.

Cite this

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

A computationally efficient approach to 3D ear recognition employing local and holistic features. / Zhou, Jindan; Cadavid, Steven; Abdel-Mottaleb, Mohamed.

2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011. 2011. 5981815 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011, Colorado Springs, CO, United States, 6/20/11. https://doi.org/10.1109/CVPRW.2011.5981815

Zhou J, Cadavid S, Abdel-Mottaleb M. 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. 2011. 5981815. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). https://doi.org/10.1109/CVPRW.2011.5981815

@inproceedings{a8be2a0eb45b4b5cb21e5f97a7caa711,

title = "A computationally efficient approach to 3D ear recognition employing local and holistic features",

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.",

author = "Jindan Zhou and Steven Cadavid and Mohamed Abdel-Mottaleb",

year = "2011",

month = oct,

day = "31",

doi = "10.1109/CVPRW.2011.5981815",

language = "English (US)",

isbn = "9781457705298",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

booktitle = "2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011",

note = "2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011 ; Conference date: 20-06-2011 Through 25-06-2011",

}

TY - GEN

T1 - A computationally efficient approach to 3D ear recognition employing local and holistic features

AU - Zhou, Jindan

AU - Cadavid, Steven

AU - Abdel-Mottaleb, Mohamed

PY - 2011/10/31

Y1 - 2011/10/31

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=80054902721&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80054902721&partnerID=8YFLogxK

U2 - 10.1109/CVPRW.2011.5981815

DO - 10.1109/CVPRW.2011.5981815

M3 - Conference contribution

AN - SCOPUS:80054902721

SN - 9781457705298

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

BT - 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011

T2 - 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2011

Y2 - 20 June 2011 through 25 June 2011

ER -