Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Adjustment Learning and Relevant Component Analysis
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Learning a Mahalanobis Metric from Equivalence Constraints
The Journal of Machine Learning Research
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning Distance Metrics with Contextual Constraints for Image Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Journal of Cognitive Neuroscience
An efficient algorithm for local distance metric learning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Distance metric learning for content identification
IEEE Transactions on Information Forensics and Security
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In this paper, we propose a unified scheme of subspace and distance metric learning under the Bayesian framework for face recognition. According to the local distribution of data, we divide the k-nearest neighbors of each sample into the intra-person set and the inter-person set, and we aim to learn a distance metric in the embedding subspace, which can make the distances between the sample and its intra-person set smaller than the distances between it and its interperson set. To reach this goal, we define two variables, that is, the intra-person distance and the inter-person distance, which are from two different probabilistic distributions, and we model the goal with minimizing the overlap between two distributions. Inspired by the Bayesian classification error estimation, we formulate it by minimizing the Bhattachyrra coefficient between two distributions. The power of the proposed approach are demonstrated by a series of experiments on the CMU-PIE face database and the extended YALE face database.