Abstract
We introduce a novel shape-based feature set, termed the Histograms of Categorized Shapes (HCS), for robust Three-Dimensional (3D) object recognition. By adopting the sliding window approach and a linear Support Vector Machine (SVM) classifier, the efficacy of the HCS feature is assessed on a 3D ear detection task. Experimental results demonstrate that the approach achieves a perfect detection rate, i.e., a 100% detection rate with a 0% false positive rate, on a validation set consisting of 142 range profile images from the University of Notre Dame (UND) 3D ear biometrics database. It is to the best of our knowledge that the detection rate achieved here outperforms those reported in the literature for the given dataset. The proposed detector is also extremely efficient in both training and detection due to the simplicity of the feature extraction and speed of the classification process, suggesting that the method is suitable for practical use in 3D ear biometric applications.
| Original language | English |
|---|---|
| Title of host publication | IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010 |
| DOIs | |
| State | Published - Dec 27 2010 |
| Event | 4th IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010 - Washington, DC, United States Duration: Sep 27 2010 → Sep 29 2010 |
Other
| Other | 4th IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010 |
|---|---|
| Country | United States |
| City | Washington, DC |
| Period | 9/27/10 → 9/29/10 |
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ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science
Cite this
Histograms of categorized shapes for 3D ear detection. / Zhou, Jindan; Cadavid, Steven; Abdel-Mottaleb, Mohamed.
IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010. 2010. 5634512.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Histograms of categorized shapes for 3D ear detection
AU - Zhou, Jindan
AU - Cadavid, Steven
AU - Abdel-Mottaleb, Mohamed
PY - 2010/12/27
Y1 - 2010/12/27
N2 - We introduce a novel shape-based feature set, termed the Histograms of Categorized Shapes (HCS), for robust Three-Dimensional (3D) object recognition. By adopting the sliding window approach and a linear Support Vector Machine (SVM) classifier, the efficacy of the HCS feature is assessed on a 3D ear detection task. Experimental results demonstrate that the approach achieves a perfect detection rate, i.e., a 100% detection rate with a 0% false positive rate, on a validation set consisting of 142 range profile images from the University of Notre Dame (UND) 3D ear biometrics database. It is to the best of our knowledge that the detection rate achieved here outperforms those reported in the literature for the given dataset. The proposed detector is also extremely efficient in both training and detection due to the simplicity of the feature extraction and speed of the classification process, suggesting that the method is suitable for practical use in 3D ear biometric applications.
AB - We introduce a novel shape-based feature set, termed the Histograms of Categorized Shapes (HCS), for robust Three-Dimensional (3D) object recognition. By adopting the sliding window approach and a linear Support Vector Machine (SVM) classifier, the efficacy of the HCS feature is assessed on a 3D ear detection task. Experimental results demonstrate that the approach achieves a perfect detection rate, i.e., a 100% detection rate with a 0% false positive rate, on a validation set consisting of 142 range profile images from the University of Notre Dame (UND) 3D ear biometrics database. It is to the best of our knowledge that the detection rate achieved here outperforms those reported in the literature for the given dataset. The proposed detector is also extremely efficient in both training and detection due to the simplicity of the feature extraction and speed of the classification process, suggesting that the method is suitable for practical use in 3D ear biometric applications.
UR - http://www.scopus.com/inward/record.url?scp=78650371608&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78650371608&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2010.5634512
DO - 10.1109/BTAS.2010.5634512
M3 - Conference contribution
AN - SCOPUS:78650371608
SN - 9781424475803
BT - IEEE 4th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2010
ER -