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 |
|---|---|
| Title of host publication | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
| DOIs | |
| 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 |
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 |
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ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering
Cite this
A computationally efficient approach to 3D ear recognition employing local and holistic features. / Zhou, Jindan; Cadavid, Steven; Abdel-Mottaleb, Mohamed.
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2011. 5981815.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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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
SN - 9781457705298
BT - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ER -