Three-dimensional computer vision: a geometric viewpoint
Three-dimensional computer vision: a geometric viewpoint
Projective registration with difference decomposition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Learning of Probabilistic Appearance Manifolds for Video-Based Recognition and Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Recognition Based on Frontal Views Generated from Non-Frontal Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Making 2D Face Recognition MoreRobust Using AAMs for Pose Compensation
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Face Recognition Robust to Head Pose from One Sample Image
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
An active model for facial feature tracking
EURASIP Journal on Applied Signal Processing
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Person-Specific Face Shape Estimation under Varying Head Pose from Single Snapshots
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Locally Linear Regression for Pose-Invariant Face Recognition
IEEE Transactions on Image Processing
On Appearance Based Face and Facial Action Tracking
IEEE Transactions on Circuits and Systems for Video Technology
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Human-machine interaction is a hot topic nowadays in the communities of computer vision and robotics. In this context, face recognition algorithms (used as primary cue for a person's identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. This is mostly due to the difficulty of simultaneously handling variations in illumination, pose, and occlusions. In this paper, we propose a novel approach for robust pose-invariant face recognition for human-robot interaction based on the real-time fitting of a 3D deformable model to input images taken from video sequences. More concrete, our approach generates a rectified face image irrespective with the actual head-pose orientation. Experimental results performed on Honda video database, using several manifold learning techniques, show a distinct advantage of the proposed method over the standard 2D appearance-based snapshot approach.