TY - GEN
T1 - Robust head pose estimation using supervised manifold projection
AU - Wang, Chao
AU - Song, Xubo
PY - 2012
Y1 - 2012
N2 - Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with pose being the only variable, the facial images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background, facial expression, and illumination. This problem may be alleviated by incorporating the pose angle information of training samples into the manifold learning process. In this paper, we propose a supervised neighborhood-based linear feature transformation algorithm, which is a variant of Fisher Discriminant Analysis (FDA), to constrain the projection computation of manifold learning. The experimental results show that our algorithm improves the accuracy and robustness of head pose estimation.
AB - Head poses can be automatically estimated using manifold learning algorithms, with the assumption that with pose being the only variable, the facial images should lie in a smooth and low-dimensional manifold. However, this estimation approach is challenging due to other appearance variations related to identity, head location in image, background, facial expression, and illumination. This problem may be alleviated by incorporating the pose angle information of training samples into the manifold learning process. In this paper, we propose a supervised neighborhood-based linear feature transformation algorithm, which is a variant of Fisher Discriminant Analysis (FDA), to constrain the projection computation of manifold learning. The experimental results show that our algorithm improves the accuracy and robustness of head pose estimation.
KW - Head pose estimation
KW - manifold learning
KW - projection computation
KW - supervised neighborhood-based FDA
UR - http://www.scopus.com/inward/record.url?scp=84875819889&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84875819889&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2012.6466820
DO - 10.1109/ICIP.2012.6466820
M3 - Conference contribution
AN - SCOPUS:84875819889
SN - 9781467325332
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 161
EP - 164
BT - 2012 IEEE International Conference on Image Processing, ICIP 2012 - Proceedings
T2 - 2012 19th IEEE International Conference on Image Processing, ICIP 2012
Y2 - 30 September 2012 through 3 October 2012
ER -