Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
SFS Based View Synthesis for Robust Face Recognition
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face recognition from one example view
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Appearance-Based Face Recognition and Light-Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pose-Robust Face Recognition Using Geometry Assisted Probabilistic Modeling
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
Regression and Classification Approaches to Eye Localization in Face Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Face Recognition based on a 3D Morphable Model
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Learning Patch Dependencies for Improved Pose Mismatched Face Verification
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On transforming statistical models for non-frontal face verification
Pattern Recognition
Face refinement through a gradient descent alignment approach
VisHCI '06 Proceedings of the HCSNet workshop on Use of vision in human-computer interaction - Volume 56
Local feature hashing for face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Probabilistic learning for fully automatic face recognition across pose
Image and Vision Computing
Object recognition using words model of optimal size: in histograms of oriented gradients
IITA'09 Proceedings of the 3rd international conference on Intelligent information technology application
Robust pose invariant face recognition using coupled latent space discriminant analysis
Computer Vision and Image Understanding
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Sparsely registering a face (i.e., locating 2---3 fiducial points) is considered a much easier task than densely registering one; especially with varying viewpoints. Unfortunately, the converse tends to be true for the task of viewpoint-invariant face verification; the more registration points one has the better the performance. In this paper we present a novel approach to viewpoint invariant face verification which we refer to as the "patch-whole" algorithm. The algorithm is able to obtain good verification performance with sparsely registered faces. Good performance is achieved by not assuming any alignment between gallery and probe view faces, but instead trying to learn the joint likelihood functions for faces of similar and dissimilar identities. Generalization is encouraged by factorizing the joint gallery and probe appearance likelihood, for each class, into an ensemble of "patch-whole" likelihoods. We make an additional contribution in this paper by reviewing existing approaches to viewpoint-invariant face verification and demonstrating how most of them fall into one of two categories; namely viewpoint-generative or viewpoint-discriminative. This categorization is instructive as it enables us to compare our "patch-whole" algorithm to other paradigms in viewpoint-invariant face verification and also gives deeper insights into why the algorithm performs so well.