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.
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
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.
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U2 - 10.1109/ICME.2006.262697
DO - 10.1109/ICME.2006.262697
M3 - Conference contribution
AN - SCOPUS:34247606573
SN - 1424403677
SN - 9781424403677
T3 - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
SP - 981
EP - 984
BT - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006 - Proceedings
T2 - 2006 IEEE International Conference on Multimedia and Expo, ICME 2006
Y2 - 9 July 2006 through 12 July 2006
ER -