Fundamentals of digital image processing
Fundamentals of digital image processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum Risk Distance Measure for Object Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Rapid and brief communication: Two-dimensional FLD for face recognition
Pattern Recognition
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A discriminant analysis using composite features for classification problems
Pattern Recognition
Block principal component analysis with L1-norm for image analysis
Pattern Recognition Letters
Pixel selection based on discriminant features with application to face recognition
Pattern Recognition Letters
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This paper proposes a new subspace method that is based on image covariance obtained from windowed features of images. A windowed input feature consists of a number of pixels, and the dimension of input space is determined by the number of windowed features. Each element of an image covariance matrix can be obtained from the inner product of two windowed features. The 2D-PCA and 2D-LDA methods are then obtained from principal component analysis and linear discriminant analysis, respectively, using the image covariance matrix. In the case of 2D-LDA, there is no need for PCA preprocessing and the dimension of subspace can be greater than the number of classes because the within-class and between-class image covariance matrices have full ranks. Comparative experiments are performed using the FERET, CMU, and ORL databases of facial images. The experimental results show that the proposed 2D-LDA provides the best recognition rate among several subspace methods in all of the tests.