Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time face pose estimation
Real-Time Imaging - Special issue on real-time visual monitoring and inspection
Estimating facial pose using shape-from-shading
Pattern Recognition Letters
Supervised dimension reduction of intrinsically low-dimensional data
Neural Computation
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
3-D Facial Pose and Gaze Point Estimation Using a Robust Real-Time Tracking Paradigm
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Detecting Head Pose from Stereo Image Sequence for Active Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comparative Study of Coarse Head Pose Estimation
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Head Pose Estimation Using View Based Eigenspaces
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Head Pose Estimation using Fisher Manifold Learning
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Active Appearance Models Revisited
International Journal of Computer Vision
A Probabilistic Framework for Joint Head Tracking and Pose Estimation
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Head Pose Estimation by Nonlinear Manifold Learning
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment
SIAM Journal on Scientific Computing
Head Pose Estimation of Partially Occluded Faces
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Graph Embedded Analysis for Head Pose Estimation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
An Improved SNoW Based Classification Technique for Head-pose Estimation and Face Detection
AIPR '05 Proceedings of the 34th Applied Imagery and Pattern Recognition Workshop
Supervised Isomap with Explicit Mapping
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 3
Pattern Recognition
Supervised locally linear embedding
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Estimating face pose by facial asymmetry and geometry
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Supervised nonlinear dimensionality reduction for visualization and classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Optimal regularization parameter estimation for spectral regression discriminant analysis
IEEE Transactions on Circuits and Systems for Video Technology
Monocular head pose estimation using generalized adaptive view-based appearance model
Image and Vision Computing
Robust head pose estimation using supervised manifold learning
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
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Head pose estimation has been an integral problem in the study of face recognition systems and human-computer interfaces, as part of biometric applications. A fine estimate of the head pose angle is necessary and useful for several face analysis applications. To determine the head pose, face images with varying pose angles can be considered to be lying on a smooth low-dimensional manifold in high-dimensional image feature space. However, when there are face images of multiple individuals with varying pose angles, manifold learning techniques often do not give accurate results. In this work, we propose a framework for a supervised formof manifold learning called Biased Manifold Embedding to obtain improved performance in head pose angle estimation. This framework goes beyond pose estimation, and can be applied to all regression applications. This framework, although formulated for a regression scenario, unifies other supervised approaches to manifold learning that have been proposed so far. Detailed studies of the proposed method are carried out on the FacePix database, which contains 181 face images each of 30 individuals with pose angle variations at a granularity of 1. Since biometric applications in the real world may not contain this level of granularity in training data, an analysis of the methodology is performed on sparsely sampled data to validate its eectiveness. We obtained up to 2average pose angle estimation error in the results from our experiments, which matched the best results obtained for head pose estimation using related approaches.