On partial least squares in head pose estimation: How to simultaneously deal with misalignment

Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified m...

Full description

Saved in:
Bibliographic Details
Published in2012 IEEE Conference on Computer Vision and Pattern Recognition pp. 2602 - 2609
Main Authors Haj, M. A., Gonzalez, J., Davis, L. S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2012
Subjects
Estimation
Face
Kernel
Magnetic heads
Matrix decomposition
Vectors
Online AccessGet full text

Cover

More Information
Summary:Head pose estimation is a critical problem in many computer vision applications. These include human computer interaction, video surveillance, face and expression recognition. In most prior work on heads pose estimation, the positions of the faces on which the pose is to be estimated are specified manually. Therefore, the results are reported without studying the effect of misalignment. We propose a method based on partial least squares (PLS) regression to estimate pose and solve the alignment problem simultaneously. The contributions of this paper are two-fold: 1) we show that the kernel version of PLS (kPLS) achieves better than state-of-the-art results on the estimation problem and 2) we develop a technique to reduce misalignment based on the learned PLS factors.
ISBN:9781467312264
1467312266
ISSN:1063-6919
DOI:10.1109/CVPR.2012.6247979