Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Comparative Study of Coarse Head Pose Estimation
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Extended Isomap for Classification
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Absolute Head Pose Estimation From Overhead Wide-Angle Cameras
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Head Pose Estimation by Nonlinear Manifold Learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Supervised Isomap with Explicit Mapping
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Regression on manifolds using kernel dimension reduction
Proceedings of the 24th international conference on Machine learning
A two-stage head pose estimation framework and evaluation
Pattern Recognition
Person-independent head pose estimation using biased manifold embedding
EURASIP Journal on Advances in Signal Processing
A Supervised Subspace Learning Algorithm: Supervised Neighborhood Preserving Embedding
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
Illumination and Person-Insensitive Head Pose Estimation Using Distance Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph embedding with constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Out-of-Sample embedding of spherical manifold based on constrained least squares
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
A Two-Layer Framework for Piecewise Linear Manifold-Based Head Pose Estimation
International Journal of Computer Vision
Facial landmark localization based on hierarchical pose regression with cascaded random ferns
Proceedings of the 21st ACM international conference on Multimedia
Robust frontal view search using extended manifold learning
Journal of Visual Communication and Image Representation
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We address the problem of fine-grain head pose angle estimation from a single 2D face image as a continuous regression problem. Currently the state of the art, and a promising line of research, on head pose estimation seems to be that of nonlinear manifold embedding techniques, which learn an "optimal" low-dimensional manifold that models the nonlinear and continuous variation of face appearance with pose angle. Furthermore, supervised manifold learning techniques attempt to achieve this robustly in the presence of latent variables in the training set (especially identity, illumination, and facial expression), by incorporating head pose angle information accompanying the training samples. Most of these techniques are designed with the classification scenario in mind, however, and are not directly applicable to the regression scenario where continuous numeric values (pose angles), rather than class labels (discrete poses), are available. In this paper, we propose to deal with the regression case in a principled way. We present a taxonomy of methods for incorporating continuous pose angle information into one or more stages of the manifold learning process, and discuss its implementation for Neighborhood Preserving Embedding (NPE) and Locality Preserving Projection (LPP). Experiments are carried out on a face dataset containing significant identity and illumination variations, and the results show that our regression-based approach far outperforms previous supervised manifold learning methods for head pose estimation.