Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Nearest Linear Combinations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
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
Rectified nearest feature line segment for pattern classification
Pattern Recognition
Interest Operator versus Gabor filtering for facial imagery classification
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction using constrained maximum variance mapping
Pattern Recognition
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative subspace analysis based on feature line distance
IEEE Transactions on Image Processing
A feature extraction method for use with bimodal biometrics
Pattern Recognition
Direct sparse nearest feature classifier for face recognition
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and simulation and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part III
Linear Regression for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Local Linear Discriminant Analysis Framework Using Sample Neighbors
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
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The nearest subspace (NS) classification is an efficient method to solve face recognition problem by using the linear regression technique. This method is based on the assumption that face images from a specific subject class tend to span a unique subspace, i.e. a class-specific subspace. Then, a test image has the shortest distance from its own class-specific subspace. In this paper, we present a novel idea for face recognition. This idea considers that a test face image should be far from the farthest subspace (FS) spanned by all training images except the images from the class of this test image. Based on this idea, we propose the FS classifier for face recognition. In our opinion, NS and FS classifiers take advantages of different characteristics of the class-specific subspace. NS classifier exploits the relationship between a test image and a single class while FS classifier measures relationship between this test image and the rest classes. Consequently, we propose the nearest-farthest subspace (NFS) classifier which exploits the both relationships to classify a test image. The comparisons with NS classifier and other state-of-the-art methods on four famous public face databases demonstrate the good performance of FS and NFS.