Abstract
We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. Using the extracted 2D features plus their corresponding disparities in the disparity map, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project it's vertices on the frontal 2D image and adjust its profile line vertices using the profile view. We increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject's face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate.
| Original language | English |
|---|---|
| Title of host publication | 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings |
| Pages | 981-984 |
| Number of pages | 4 |
| Volume | 2006 |
| DOIs | |
| State | Published - Dec 1 2006 |
| Event | 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Toronto, ON, Canada Duration: Jul 9 2006 → Jul 12 2006 |
Other
| Other | 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 |
|---|---|
| Country | Canada |
| City | Toronto, ON |
| Period | 7/9/06 → 7/12/06 |
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ASJC Scopus subject areas
- Media Technology
- Electrical and Electronic Engineering
Cite this
Disparity-based 3D face modeling using 3D deformable facial mask for 3D face recognition. / Ansari, A. Nasser; Abdel-Mottaleb, Mohamed; Mahoor, Mohammad H.
2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings. Vol. 2006 2006. p. 981-984 4036766.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Disparity-based 3D face modeling using 3D deformable facial mask for 3D face recognition
AU - Ansari, A. Nasser
AU - Abdel-Mottaleb, Mohamed
AU - Mahoor, Mohammad H.
PY - 2006/12/1
Y1 - 2006/12/1
N2 - We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. Using the extracted 2D features plus their corresponding disparities in the disparity map, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project it's vertices on the frontal 2D image and adjust its profile line vertices using the profile view. We increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject's face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate.
AB - We present an automatic disparity-based approach for 3D face modeling, from two frontal and one profile view stereo images, for 3D face recognition applications. Once the images are captured, the algorithm starts by extracting selected 2D facial features from one of the frontal views and computes a dense disparity map from the two frontal images. Using the extracted 2D features plus their corresponding disparities in the disparity map, we compute their 3D coordinates. We next align a low resolution 3D mesh model to the 3D features, re-project it's vertices on the frontal 2D image and adjust its profile line vertices using the profile view. We increase the resolutions of the resulting 2D model only at its center region to obtain a facial mask model covering distinctive features of the face. The computation of the 2D vertices coordinates with their disparities results in a deformed 3D model mask specific to a give subject's face. Application of the model in 3D face recognition validates the algorithm and shows a high recognition rate.
UR - http://www.scopus.com/inward/record.url?scp=34247606573&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34247606573&partnerID=8YFLogxK
U2 - 10.1109/ICME.2006.262697
DO - 10.1109/ICME.2006.262697
M3 - Conference contribution
SN - 1424403677
SN - 9781424403677
VL - 2006
SP - 981
EP - 984
BT - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
ER -