Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Margin based feature selection - theory and algorithms
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Boosting as a Regularized Path to a Maximum Margin Classifier
The Journal of Machine Learning Research
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
How boosting the margin can also boost classifier complexity
ICML '06 Proceedings of the 23rd international conference on Machine learning
Recognize High Resolution Faces: From Macrocosm to Microcosm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical ensemble of global and local classifiers for face recognition
IEEE Transactions on Image Processing
Image and Vision Computing
Boosting through optimization of margin distributions
IEEE Transactions on Neural Networks
On the Dual Formulation of Boosting Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Similarity scores based on background samples
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
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
Maximizing all margins: Pushing face recognition with Kernel Plurality
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
With one look: robust face recognition using single sample per person
Proceedings of the 21st ACM international conference on Multimedia
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Small sample size is one of the most challenging problems in face recognition due to the difficulty of sample collection in many real-world applications. By representing the query sample as a linear combination of training samples from all classes, the so-called collaborative representation based classification (CRC) shows very effective face recognition performance with low computational cost. However, the recognition rate of CRC will drop dramatically when the available training samples per subject are very limited. One intuitive solution to this problem is operating CRC on patches and combining the recognition outputs of all patches. Nonetheless, the setting of patch size is a non-trivial task. Considering the fact that patches on different scales can have complementary information for classification, we propose a multi-scale patch based CRC method, while the ensemble of multi-scale outputs is achieved by regularized margin distribution optimization. Our extensive experiments validated that the proposed method outperforms many state-of-the-art patch based face recognition algorithms.