Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Atomic Decomposition by Basis Pursuit
SIAM Review
Discriminant Waveletfaces and Nearest Feature Classifiers for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A faster algorithm for ridge regression of reduced rank data
Computational Statistics & Data Analysis
Novel Adaptive Nearest Neighbor Classifiers Based On Hit-Distance
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Regression for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
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The classical k-NN classifier has been widely used in pattern recognition. However, it does not take into account the structural information of local samples. This paper presents a novel classifier named component-based global k-NN classifier (CG-k-NN), which takes advantage of the structural information of the local neighbors for enhancing the classification performance. We choose k nearest neighbors of a given testing sample globally at first, and then use these neighbors to represent the testing sample via ridge regression. In the further step, we construct the component image of each class by using the intra-class images from the k nearest neighbors and the corresponding representation coefficients. Finally, the testing sample is assigned to the class that minimizes reconstruction residual. The proposed method CG-k-NN is evaluated using the ORL, FERET, AR face image database and PolyU palmprint databases. The experiment results demonstrate that our method is more efficient and effective than the state-of-the-art methods such as sparse representation based classifier (SRC) and linear regression based classifier (LRC).