Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Improved system for object detection and star/galaxy classification via local subspace analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Feature Reduction via Generalized Uncorrelated Linear Discriminant Analysis
IEEE Transactions on Knowledge and Data Engineering
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
Down-Sampling Face Images and Low-Resolution Face Recognition
ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
Discriminative Locality Alignment
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Invariant Image Watermarking Based on Local Feature Regions
CW '08 Proceedings of the 2008 International Conference on Cyberworlds
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Mean for Subspace Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Pattern Recognition Technologies with Applications to Biometrics
Advanced Pattern Recognition Technologies with Applications to Biometrics
Using Local Dependencies within Batches to Improve Large Margin Classifiers
The Journal of Machine Learning Research
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Rapid and brief communications: Modified linear discriminant analysis
Pattern Recognition
Manifold learning for video-to-video face recognition
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Discriminant feature extraction based on center distance
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Face Recognition Using a Multi-manifold Discriminant Analysis Method
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Max-Min Distance Analysis by Using Sequential SDP Relaxation for Dimension Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Is face recognition really a Compressive Sensing problem?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Are sparse representations really relevant for image classification?
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Non-Negative Patch Alignment Framework
IEEE Transactions on Neural Networks
Sparse representation or collaborative representation: Which helps face recognition?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
A competitive sample selection method for palmprint recognition
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
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In this paper we propose a supervised sparse representation method for face recognition. We assume that the test sample could be approximately represented by a sparse linear combination of all the training samples, where the term ''sparse'' means that in the linear combination most training samples have zero coefficients. We exploit a heuristic strategy to achieve this goal. First, we determine a linear combination of all the training samples that best represents the test sample and delete the training sample whose coefficient has the minimum absolute value. Then a similar procedure is carried out for the remaining training samples and this procedure is repeatedly carried out till the predefined termination condition is satisfied. The finally remaining training samples are used to produce a best representation of the test sample and to classify it. The face recognition experiments show that the proposed method can achieve promising classification accuracy.