Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Matrix algorithms
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
Introducing a weighted non-negative matrix factorization for image classification
Pattern Recognition Letters
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Nonsmooth Nonnegative Matrix Factorization (nsNMF)
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Modified Non-negative Matrix Factorization Algorithm for Face Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Learning a Maximum Margin Subspace for Image Retrieval
IEEE Transactions on Knowledge and Data Engineering
Orthogonal neighborhood preserving discriminant analysis for face recognition
Pattern Recognition
Non-negative Matrix Factorization on Manifold
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Doubly weighted nonnegative matrix factorization for imbalanced face recognition
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Classification and Feature Extraction by Simplexization
IEEE Transactions on Information Forensics and Security
A Parameter-Free Framework for General Supervised Subspace Learning
IEEE Transactions on Information Forensics and Security
IEEE Transactions on Information Forensics and Security - Part 2
Face recognition using the weighted fractal neighbor distance
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image
IEEE Transactions on Image Processing
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
A Comparative Study of Local Matching Approach for Face Recognition
IEEE Transactions on Image Processing
Topology Preserving Non-negative Matrix Factorization for Face Recognition
IEEE Transactions on Image Processing
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
Locality-Preserved Maximum Information Projection
IEEE Transactions on Neural Networks
Nonnegative Matrix Factorization in Polynomial Feature Space
IEEE Transactions on Neural Networks
Gait-based human age estimation
IEEE Transactions on Information Forensics and Security
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We propose in this paper a doubly weighted subspace learning approach for face representation and recognition. Motivated by the fact that some face samples and parts are more effectual in characterizing and recognizing faces, we construct two weighting matrices based on pairwise similarity of face samples within a same class and discriminant score of each pixel within a face sample to duly emphasize both the between-sample and within-sample features. We then incorporate these two weighting matrices into three popular subspace learning methods, namely principal component analysis, linear discriminant analysis, and nonnegative matrix factorization, to obtain the discriminative features of faces for recognition. Moreover, the proposed doubly weighted technique can be readily extended to other newly proposed subspace learning algorithms to improve their performance. Experimental results show that the proposed approach can effectively enhance the discriminant power of the extracted face features and outperform existing, nonweighted subspace learning algorithms. The performance gain is even more apparent for cases with imbalanced training samples.