Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian face recognition using Gabor features
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Subspace Analysis Using Random Mixture Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Information-theoretic metric learning
Proceedings of the 24th international conference on Machine learning
Face Alignment Via Component-Based Discriminative Search
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Bayesian face recognition using support vector machine and face clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Cosine similarity metric learning for face verification
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Probabilistic Models for Inference about Identity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic linear discriminant analysis
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Distance metric learning with eigenvalue optimization
The Journal of Machine Learning Research
Modeling the joint density of two images under a variety of transformations
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
The case for onloading continuous high-datarate perception to the phone
HotOS'13 Proceedings of the 14th USENIX conference on Hot Topics in Operating Systems
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In this paper, we revisit the classical Bayesian face recognition method by Baback Moghaddam et al. and propose a new joint formulation. The classical Bayesian method models the appearance difference between two faces. We observe that this "difference" formulation may reduce the separability between classes. Instead, we model two faces jointly with an appropriate prior on the face representation. Our joint formulation leads to an EM-like model learning at the training time and an efficient, closed-formed computation at the test time. On extensive experimental evaluations, our method is superior to the classical Bayesian face and many other supervised approaches. Our method achieved 92.4% test accuracy on the challenging Labeled Face in Wild (LFW) dataset. Comparing with current best commercial system, we reduced the error rate by 10%.