TY - GEN
T1 - 3D face pose and animation tracking via eigen-decomposition based Bayesian approach
AU - Tran, Ngoc Trung
AU - Ababsa, Fakhr Eddine
AU - Charbit, Maurice
AU - Feldmar, Jacques
AU - Petrovska-Delacrétaz, Dijana
AU - Chollet, Gérard
PY - 2013/1/1
Y1 - 2013/1/1
N2 - This paper presents a new method to track both the face pose and the face animation with a monocular camera. The approach is based on the 3D face model CANDIDE and on the SIFT (Scale Invariant Feature Transform) descriptors, extracted around a few given landmarks (26 selected vertices of CANDIDE model) with a Bayesian approach. The training phase is performed on a synthetic database generated from the first video frame. At each current frame, the face pose and animation parameters are estimated via a Bayesian approach, with a Gaussian prior and a Gaussian likelihood function whose the mean and the covariance matrix eigenvalues are updated from the previous frame using eigen decomposition. Numerical results on pose estimation and landmark locations are reported using the Boston University Face Tracking (BUFT) database and Talking Face video. They show that our approach, compared to six other published algorithms, provides a very good compromise and presents a promising perspective due to the good results in terms of landmark localization.
AB - This paper presents a new method to track both the face pose and the face animation with a monocular camera. The approach is based on the 3D face model CANDIDE and on the SIFT (Scale Invariant Feature Transform) descriptors, extracted around a few given landmarks (26 selected vertices of CANDIDE model) with a Bayesian approach. The training phase is performed on a synthetic database generated from the first video frame. At each current frame, the face pose and animation parameters are estimated via a Bayesian approach, with a Gaussian prior and a Gaussian likelihood function whose the mean and the covariance matrix eigenvalues are updated from the previous frame using eigen decomposition. Numerical results on pose estimation and landmark locations are reported using the Boston University Face Tracking (BUFT) database and Talking Face video. They show that our approach, compared to six other published algorithms, provides a very good compromise and presents a promising perspective due to the good results in terms of landmark localization.
U2 - 10.1007/978-3-642-41914-0_55
DO - 10.1007/978-3-642-41914-0_55
M3 - Conference contribution
AN - SCOPUS:84888227014
SN - 9783642419133
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 562
EP - 571
BT - Advances in Visual Computing - 9th International Symposium, ISVC 2013, Proceedings
PB - Springer Verlag
T2 - 9th International Symposium on Advances in Visual Computing, ISVC 2013
Y2 - 29 July 2013 through 31 July 2013
ER -