Illumination and Person-Insensitive Head Pose Estimation Using Distance Metric Learning

Head pose estimation is an important task for many face analysis applications, such as face recognition systems and human computer interactions. In this paper we aim to address the pose estimation problem under some challenging conditions, e.g., from a single image, large pose variation, and un-even illumination conditions. The approach we developed combines non-linear dimension reduction techniques with a learned distance metric transformation. The learned distance metric provides better intra-class clustering, therefore preserving a smooth low-dimensional manifold in the presence of large variation in the input images due to illumination changes. Experiments show that our method improves the performance, achieving accuracy within 2-3 degrees for face images with varying poses and within 3-4 degrees error for face images with varying pose and illumination changes.

[1]  Yuxiao Hu,et al.  Estimating face pose by facial asymmetry and geometry , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[2]  Tomer Hertz,et al.  Learning a Mahalanobis Metric from Equivalence Constraints , 2005, J. Mach. Learn. Res..

[3]  Pietro Perona,et al.  Self-Tuning Spectral Clustering , 2004, NIPS.

[4]  Michael I. Jordan,et al.  Distance Metric Learning with Application to Clustering with Side-Information , 2002, NIPS.

[5]  Michael E. Tipping.  The Relevance Vector Machine , 1999, NIPS.

[6]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[7]  Stan Z. Li,et al.  Learning multiview face subspaces and facial pose estimation using independent component analysis , 2005, IEEE Transactions on Image Processing.

[8]  Matti Pietikäinen,et al.  Supervised Locally Linear Embedding , 2003, ICANN.

[9]  Sethuraman Panchanathan,et al.  Person-Independent Head Pose Estimation Using Biased Manifold Embedding , 2008, EURASIP J. Adv. Signal Process..

[10]  Geoffrey E. Hinton,et al.  Neighbourhood Components Analysis , 2004, NIPS.

[11]  Jun Guo,et al.  Supervised Isomap with Explicit Mapping , 2006, First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06).

[12]  Zhen Yang,et al.  A Version of Isomap with Explicit Mapping , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[13]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[14]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[15]  Yuxiao Hu,et al.  Head pose estimation using Fisher Manifold learning , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[16]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[17]  Kilian Q. Weinberger,et al.  Distance Metric Learning for Large Margin Nearest Neighbor Classification , 2005, NIPS.

[18]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[19]  Ming-Hsuan Yang.  Extended Isomap for classification , 2002, Object recognition supported by user interaction for service robots.

[20]  Yun Fu,et al.  Graph embedded analysis for head pose estimation , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[21]  Sethuraman Panchanathan,et al.  Biased Manifold Embedding: A Framework for Person-Independent Head Pose Estimation , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[23]  Shaogang Gong,et al.  Real-time face pose estimation , 1998, Real Time Imaging.

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

[25]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.

[26]  Jean-Marc Odobez,et al.  A probabilistic framework for joint head tracking and pose estimation , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[27]  Matthew Turk,et al.  A Morphable Model For The Synthesis Of 3D Faces , 1999, SIGGRAPH.

[28]  Masashi Sugiyama,et al.  Local Fisher discriminant analysis for supervised dimensionality reduction , 2006, ICML.