Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
An Investigation into Face Pose Distributions
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Face Recognition from Unfamiliar Views: Subspace Methods and Pose Dependency
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
View-Based Active Appearance Models
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Pose Invariant Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Think globally, fit locally: unsupervised learning of low dimensional manifolds
The Journal of Machine Learning Research
Handbook of Face Recognition
Local Linear Regression (LLR) for Pose Invariant Face Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Optimal Pose for Face Recognition
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Face Recognition Robust to Head Pose from One Sample Image
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Robust frontal view search using extended manifold learning
Journal of Visual Communication and Image Representation
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This research presents a study of the geometry of the face manifold as a person changes their horizontal pose from one profile to another. Although, a lot of research has gone into aspects of determining an ideal pose for pose invariant face recognition, less has been done to present the manifold of the faces presented by these pose variations. The novelty of our approach lies in the presentation of a finely sampled profile-to-profile dataset that is analyzed using Locally Linear Embedding (LLE) to estimate the curvature of these manifolds. Our results indicate that the profile-to-profile manifold is less curved, hence more linear, in the region around the frontal view than for any other region of the manifold, i.e. pose.