Abstract
In this paper, we present a computationally efficient statistical face modeling approach. The efficiency of our proposed approach is the result of mathematical simplifications in the core formula of a previous face modeling method and the use of the singular value decomposition. In order to reduce the errors in our resulting models, we preprocess the facial images to normalize for pose and illumination and remove little occlusions. Then, the statistical face models for the enrolled subjects are obtained from the normalized face images. The effects of the variations in pose, facial expression, and illumination on the accuracy of the system are studied. Experimental results demonstrate the reduction in the computational complexity of the new approach and its efficacy in modeling the face images.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM |
| Publisher | IEEE Computer Society |
| Pages | 126-131 |
| Number of pages | 6 |
| Volume | 2015-January |
| Edition | January |
| DOIs | |
| State | Published - Jan 19 2015 |
| Event | 2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014 - Orlando, United States Duration: Dec 9 2014 → Dec 12 2014 |
Other
| Other | 2014 IEEE Symposium Series on Computational Intelligence, IEEE SSCI 2014 - 2014 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management, CIBIM 2014 |
|---|---|
| Country | United States |
| City | Orlando |
| Period | 12/9/14 → 12/12/14 |
Fingerprint
ASJC Scopus subject areas
- Biotechnology
- Artificial Intelligence
- Computational Theory and Mathematics
Cite this
Computationally efficient statistical face model in the feature space. / Haghighat, Mohammad; Abdel-Mottaleb, Mohamed; Alhalabi, Wadee.
IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM. Vol. 2015-January January. ed. IEEE Computer Society, 2015. p. 126-131 7015453.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Computationally efficient statistical face model in the feature space
AU - Haghighat, Mohammad
AU - Abdel-Mottaleb, Mohamed
AU - Alhalabi, Wadee
PY - 2015/1/19
Y1 - 2015/1/19
N2 - In this paper, we present a computationally efficient statistical face modeling approach. The efficiency of our proposed approach is the result of mathematical simplifications in the core formula of a previous face modeling method and the use of the singular value decomposition. In order to reduce the errors in our resulting models, we preprocess the facial images to normalize for pose and illumination and remove little occlusions. Then, the statistical face models for the enrolled subjects are obtained from the normalized face images. The effects of the variations in pose, facial expression, and illumination on the accuracy of the system are studied. Experimental results demonstrate the reduction in the computational complexity of the new approach and its efficacy in modeling the face images.
AB - In this paper, we present a computationally efficient statistical face modeling approach. The efficiency of our proposed approach is the result of mathematical simplifications in the core formula of a previous face modeling method and the use of the singular value decomposition. In order to reduce the errors in our resulting models, we preprocess the facial images to normalize for pose and illumination and remove little occlusions. Then, the statistical face models for the enrolled subjects are obtained from the normalized face images. The effects of the variations in pose, facial expression, and illumination on the accuracy of the system are studied. Experimental results demonstrate the reduction in the computational complexity of the new approach and its efficacy in modeling the face images.
UR - http://www.scopus.com/inward/record.url?scp=84937924440&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937924440&partnerID=8YFLogxK
U2 - 10.1109/CIBIM.2014.7015453
DO - 10.1109/CIBIM.2014.7015453
M3 - Conference contribution
VL - 2015-January
SP - 126
EP - 131
BT - IEEE Workshop on Computational Intelligence in Biometrics and Identity Management, CIBIM
PB - IEEE Computer Society
ER -