A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Multirate systems and filter banks
Multirate systems and filter banks
Local discriminant bases and their applications
Journal of Mathematical Imaging and Vision - Special issue on mathematical imaging
Frequency-Based Nonrigid Motion Analysis: Application to Four Dimensional Medical Images
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
A distance and angle similarity measure method
Journal of the American Society for Information Science
The FERET Evaluation Methodology for Face-Recognition Algorithms
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
Feature extraction by shape-adapted local discriminant bases
Signal Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Localization of Objects in the Wavelet Domain
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
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
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Where Are Linear Feature Extraction Methods Applicable?
IEEE Transactions on Pattern Analysis and Machine Intelligence
PCA vs. Automatically Pruned Wavelet-Packet PCA for Illumination Tolerant Face Recognition
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Multiresolution face recognition
Image and Vision Computing
The application of neural network and wavelet in human face illumination compensation
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Similarity-based online feature selection in content-based image retrieval
IEEE Transactions on Image Processing
Principal components null space analysis for image and video classification
IEEE Transactions on Image Processing
Face recognition using recursive Fisher linear discriminant
IEEE Transactions on Image Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Face recognition by applying wavelet subband representation and kernel associative memory
IEEE Transactions on Neural Networks
Face recognition using dual-tree complex wavelet features
IEEE Transactions on Image Processing
Meta-heuristic algorithms for optimized network flow wavelet-based image coding
Applied Soft Computing
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Face recognition is a challenging problem due to variations in pose, illumination, and expression. Techniques that can provide effective feature representation with enhanced discriminability are crucial. Wavelets have played an important role in image processing for its ability to capture localized spatial-frequency information of images. In this paper, we propose a novel local discriminant coordinates method based on wavelet packet for face recognition to compensate for these variations. Traditional wavelet-based methods for face recognition select or operate on the most discriminant subband, and neglect the scattered characteristic of discriminant features. The proposed method selects the most discriminant coordinates uniformly from all spatial frequency subbands to overcome the deficiency of traditional wavelet-based methods. To measure the discriminability of coordinates, a new dilation invariant entropy and a maximum a posterior logistic model are put forward. Moreover, a new triangle square ratio criterion is used to improve classification using the Euclidean distance and the cosine criterion. Experimental results show that the proposed method is robust for face recognition under variations in illumination, pose and expression.