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 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.

[1]  Mohan M. Trivedi,et al.  Head Pose Estimation in Computer Vision: A Survey , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Zhi-Hua Zhou,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2006, NIPS.

[3]  Shaogang Gong,et al.  Support vector regression and classification based multi-view face detection and recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[4]  James L. Crowley,et al.  Head Pose Estimation on Low Resolution Images , 2006, CLEAR.

[5]  L. Davis,et al.  Kernel PLS regression for robust monocular pose estimation , 2011, CVPR 2011 WORKSHOPS.

[6]  Mohan M. Trivedi,et al.  Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness , 2010, IEEE Transactions on Intelligent Transportation Systems.

[7]  Horst Bischof,et al.  Supervised local subspace learning for continuous head pose estimation , 2011, CVPR 2011.

[8]  Jian-Gang Wang,et al.  EM enhancement of 3D head pose estimated by point at infinity , 2007, Image Vis. Comput..

[9]  Helge J. Ritter,et al.  Recognition of human head orientation based on artificial neural networks , 1998, IEEE Trans. Neural Networks.

[10]  Roman Rosipal,et al.  Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space , 2002, J. Mach. Learn. Res..

[11]  Rainer Stiefelhagen,et al.  Neural Network-based Head Pose Estimation and Multiview Fusion – Draft Version – , 2006 .

[12]  Thomas Hofmann,et al.  Multi-Instance Multi-Label Learning with Application to Scene Classification , 2007 .

[13]  R. Stiefelhagen.  Estimating Head Pose with Neural Networks-Results on the Pointing 04 ICPR Workshop Evaluation Data , 2004 .

[14]  Yuxiao Hu,et al.  Head Pose Estimation in Seminar Room Using Multi View Face Detectors , 2006, CLEAR.

[15]  Shaogang Gong,et al.  Composite support vector machines for detection of faces across views and pose estimation , 2002, Image Vis. Comput..

[16]  Paul J. Lewi,et al.  Pattern recognition, reflections from a chemometric point of view , 1995 .

[17]  D. Jacobs,et al.  Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch , 2011, CVPR 2011.

[18]  Serge J. Belongie,et al.  Simultaneous Learning and Alignment: Multi-Instance and Multi-Pose Learning ? , 2008 .

[19]  Yuxiao Hu,et al.  Evaluation of Head Pose Estimation for Studio Data , 2006, CLEAR.

[20]  Ping Wu,et al.  Mining gene expression databases for local causal relationships using a simple constraint-based algorithm , 2006, Int. J. Pattern Recognit. Artif. Intell..

[21]  Alexander H. Waibel,et al.  Modeling focus of attention for meeting indexing based on multiple cues , 2002, IEEE Trans. Neural Networks.

[22]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[23]  Erik G. Learned-Miller,et al.  Unsupervised Joint Alignment of Complex Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[24]  S. Langton,et al.  The influence of head contour and nose angle on the perception of eye-gaze direction , 2004, Perception & psychophysics.

[25]  Larry S. Davis,et al.  Multiple instance fFeature for robust part-based object detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  J. Crowley,et al.  Estimating Face orientation from Robust Detection of Salient Facial Structures , 2004 .

[27]  Ying Wu,et al.  Query Driven Localized Linear Discriminant Models for Head Pose Estimation , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[28]  Katsuhiko Sakaue,et al.  Head pose estimation by nonlinear manifold learning , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[29]  Guodong Guo,et al.  Simultaneous dimensionality reduction and human age estimation via kernel partial least squares regression , 2011, CVPR 2011.

[30]  Qiang Ji,et al.  Improving Face Recognition by Online Image Alignment , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[31]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[32]  Larry S. Davis,et al.  Multiple instance fFeature for robust part-based object detection , 2009, CVPR.