Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Eigen Light-Fields and Face Recognition Across Pose
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The CMU Pose, Illumination, and Expression (PIE) Database
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Hierarchical Ensemble of Gabor Fisher Classifier for Face Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Tied Factor Analysis for Face Recognition across Large Pose Differences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition across pose: A review
Pattern Recognition
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Generic vs. person specific active appearance models
Image and Vision Computing
Image and Vision Computing
Wide-baseline stereo for face recognition with large pose variation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Locally Linear Regression for Pose-Invariant Face Recognition
IEEE Transactions on Image Processing
Fully automatic pose-invariant face recognition via 3D pose normalization
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Fully automatic Face Recognition Across Pose (FRAP) is one of the most desirable techniques, however, also one of the most challenging tasks in face recognition field. Matching a pair of face images in different poses can be converted into matching their pixels corresponding to the same semantic facial point. Following this idea, given two images G and P in different poses, we propose a novel method, named Morphable Displacement Field (MDF), to match G with P's virtual view under G's pose. By formulating MDF as a convex combination of a number of template displacement fields generated from a 3D face database, our model satisfies both global conformity and local consistency. We further present an approximate but effective solution of the proposed MDF model, named implicit Morphable Displacement Field (iMDF), which synthesizes virtual view implicitly via an MDF by minimizing matching residual. This formulation not only avoids intractable optimization of the high-dimensional displacement field but also facilitates a constrained quadratic optimization. The proposed method can work well even when only 2 facial landmarks are labeled, which makes it especially suitable for fully automatic FRAP system. Extensive evaluations on FERET, PIE and Multi-PIE databases show considerable improvement over state-of-the-art FRAP algorithms in both semi-automatic and fully automatic evaluation protocols.