TY - GEN
T1 - Action unit intensity estimation using hierarchical partial least squares
AU - Gehrig, Tobias
AU - Al-Halah, Ziad
AU - Ekenel, Hazim Kemal
AU - Stiefelhagen, Rainer
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/17
Y1 - 2015/7/17
N2 - Estimation of action unit (AU) intensities is considered a challenging problem. AUs exhibit high variations among the subjects due to the differences in facial plasticity and morphology. In this paper, we propose a novel framework to model the individual AUs using a hierarchical regression model. Our approach can be seen as a combination of locally linear Partial Least Squares (PLS) models where each one of them learns the relation between visual features and the AU intensity labels at different levels of details. It automatically adapts to the non-linearity in the source domain by adjusting the learned hierarchical structure. We evaluate our approach on the benchmark of the Bosphorus dataset and show that the proposed approach outperforms both the 2D state-of-the-art and the plain PLS baseline models. The generalization to other datasets is evaluated on the extended Cohn-Kanade dataset (CK+), where our hierarchical model outperforms linear and Gaussian kernel PLS.
AB - Estimation of action unit (AU) intensities is considered a challenging problem. AUs exhibit high variations among the subjects due to the differences in facial plasticity and morphology. In this paper, we propose a novel framework to model the individual AUs using a hierarchical regression model. Our approach can be seen as a combination of locally linear Partial Least Squares (PLS) models where each one of them learns the relation between visual features and the AU intensity labels at different levels of details. It automatically adapts to the non-linearity in the source domain by adjusting the learned hierarchical structure. We evaluate our approach on the benchmark of the Bosphorus dataset and show that the proposed approach outperforms both the 2D state-of-the-art and the plain PLS baseline models. The generalization to other datasets is evaluated on the extended Cohn-Kanade dataset (CK+), where our hierarchical model outperforms linear and Gaussian kernel PLS.
UR - http://www.scopus.com/inward/record.url?scp=84944909109&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944909109&partnerID=8YFLogxK
U2 - 10.1109/FG.2015.7163152
DO - 10.1109/FG.2015.7163152
M3 - Conference contribution
AN - SCOPUS:84944909109
T3 - 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
BT - 2015 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015
Y2 - 4 May 2015 through 8 May 2015
ER -