Abstract
Gender classification received attention due to its use in many applications. In this paper, the potential of using the 3D shape of the ear for gender recognition is established. We demonstrate the first attempt for gender classification from 3D ear data and evaluate different algorithms using automatically detected and aligned ears. Experiments were conducted on the University of Notre Dame (UND) database collections F and J2 which contain images with large occlusion and pose variations. It is observed that the use of Histogram of Indexed Shapes (HIS) feature along with Support Vector Machine (SVM) yields an average classification accuracy of 92.94%, which is superior to the state-of-the-art for gender classification from 2D ear images, and shows that the 3D shape of the ear comprises rich gender information.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2013 International Conference on Biometrics, ICB 2013 |
| Publisher | IEEE Computer Society |
| ISBN (Print) | 9781479903108 |
| DOIs | |
| State | Published - Jan 1 2013 |
| Event | 6th IAPR International Conference on Biometrics, ICB 2013 - Madrid, Spain Duration: Jun 4 2013 → Jun 7 2013 |
Other
| Other | 6th IAPR International Conference on Biometrics, ICB 2013 |
|---|---|
| Country | Spain |
| City | Madrid |
| Period | 6/4/13 → 6/7/13 |
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ASJC Scopus subject areas
- Biotechnology
Cite this
Gender classification using automatically detected and aligned 3D ear range data. / Lei, Jiajia; Zhou, Jindan; Abdel-Mottaleb, Mohamed.
Proceedings - 2013 International Conference on Biometrics, ICB 2013. IEEE Computer Society, 2013. 6612995.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Gender classification using automatically detected and aligned 3D ear range data
AU - Lei, Jiajia
AU - Zhou, Jindan
AU - Abdel-Mottaleb, Mohamed
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Gender classification received attention due to its use in many applications. In this paper, the potential of using the 3D shape of the ear for gender recognition is established. We demonstrate the first attempt for gender classification from 3D ear data and evaluate different algorithms using automatically detected and aligned ears. Experiments were conducted on the University of Notre Dame (UND) database collections F and J2 which contain images with large occlusion and pose variations. It is observed that the use of Histogram of Indexed Shapes (HIS) feature along with Support Vector Machine (SVM) yields an average classification accuracy of 92.94%, which is superior to the state-of-the-art for gender classification from 2D ear images, and shows that the 3D shape of the ear comprises rich gender information.
AB - Gender classification received attention due to its use in many applications. In this paper, the potential of using the 3D shape of the ear for gender recognition is established. We demonstrate the first attempt for gender classification from 3D ear data and evaluate different algorithms using automatically detected and aligned ears. Experiments were conducted on the University of Notre Dame (UND) database collections F and J2 which contain images with large occlusion and pose variations. It is observed that the use of Histogram of Indexed Shapes (HIS) feature along with Support Vector Machine (SVM) yields an average classification accuracy of 92.94%, which is superior to the state-of-the-art for gender classification from 2D ear images, and shows that the 3D shape of the ear comprises rich gender information.
UR - http://www.scopus.com/inward/record.url?scp=84887438780&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84887438780&partnerID=8YFLogxK
U2 - 10.1109/ICB.2013.6612995
DO - 10.1109/ICB.2013.6612995
M3 - Conference contribution
AN - SCOPUS:84887438780
SN - 9781479903108
BT - Proceedings - 2013 International Conference on Biometrics, ICB 2013
PB - IEEE Computer Society
ER -