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
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
On the Best Rank-1 and Rank-(R1,R2,. . .,RN) Approximation of Higher-Order Tensors
SIAM Journal on Matrix Analysis and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Best Rank-1 Approximation of Higher-Order Supersymmetric Tensors
SIAM Journal on Matrix Analysis and Applications
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized discriminant analysis for the small sample size problem in face recognition
Pattern Recognition Letters
A Comparative Study of Linear and Nonlinear Feature Extraction Methods
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Why direct LDA is not equivalent to LDA
Pattern Recognition
A Tensor Approximation Approach to Dimensionality Reduction
International Journal of Computer Vision
Neural Computing and Applications
Heteroscedastic Multilinear Discriminant Analysis for Face Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
Scene categorization based on integrated feature description and local weighted feature mapping
Computers and Electrical Engineering
Face recognition using Gabor-based direct linear discriminant analysis and support vector machine
Computers and Electrical Engineering
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In this paper, we present a novel multilinear algebra based feature extraction approach for face recognition which preserves some implicit structural or locally-spatial information among elements of the original images. We call this method three-dimensional modular discriminant analysis (3DMDA). Our approach uses a new data model called third-order tensor model (3TM) for representing the face images. In this model, each image is partitioned into the several equal size local blocks, and the local blocks are combined to represent the image as a third-order tensor. Then, a new optimization algorithm called direct mode (d-mode) is introduced for learning three optimal projection axes. Extensive experimental results conducted on four benchmark face image databases, demonstrate that 3DMDA is much more effective and robust than state-of-the-art facial feature extraction methods on both classification accuracies and computational complexities.