Optimal subset-division based discrimination and its kernelization for face and palmprint recognition

Authors:
Xiaoyuan Jing;Sheng Li;David Zhang;Chao Lan;Jingyu Yang
Affiliations:
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China and College of Automation, Nanjing University of Posts and Telecommunications, China and State Key Laboratory for Novel ...;College of Automation, Nanjing University of Posts and Telecommunications, China;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong, China;College of Automation, Nanjing University of Posts and Telecommunications, China;College of Computer Science, Nanjing University of Science and Technology, China
Venue:
Pattern Recognition
Year:
2012

Citing 28
Cited 3

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
Online Palmprint Identification

IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Methods for Pattern Analysis

Kernel Methods for Pattern Analysis
Discriminative Common Vectors for Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Subclass Discovery for Discriminant Analysis

CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Where Are Linear Feature Extraction Methods Applicable?

IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach

Neural Computation
Random Sampling for Subspace Face Recognition

International Journal of Computer Vision
Local Fisher discriminant analysis for supervised dimensionality reduction

ICML '06 Proceedings of the 23rd international conference on Machine learning
Subclass Discriminant Analysis

IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis

The Journal of Machine Learning Research
Face and palmprint pixel level fusion and Kernel DCV-RBF classifier for small sample biometric recognition

Pattern Recognition
Letters: Kernel subclass discriminant analysis

Neurocomputing
Tensor Rank One Discriminant Analysis-A convergent method for discriminative multilinear subspace selection

Neurocomputing
Perturbation LDA: Learning the difference between the class empirical mean and its expectation

Pattern Recognition
A survey of palmprint recognition

Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix

Pattern Recognition Letters
Random sampling LDA for face recognition

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Kernel Uncorrelated Adjacent-class Discriminant Analysis

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Subspace Approximation of Face Recognition Algorithms: An Empirical Study

IEEE Transactions on Information Forensics and Security
An improved LDA approach

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

IEEE Transactions on Image Processing
Improving kernel Fisher discriminant analysis for face recognition

IEEE Transactions on Circuits and Systems for Video Technology
Face recognition using kernel direct discriminant analysis algorithms

IEEE Transactions on Neural Networks

Palmprint matching using feature points and SVD factorisation

Digital Signal Processing
Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction

Pattern Recognition
Histogram of visual words based on locally adaptive regression kernels descriptors for image feature extraction

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Discriminant analysis is effective in extracting discriminative features and reducing dimensionality. In this paper, we propose an optimal subset-division based discrimination (OSDD) approach to enhance the classification performance of discriminant analysis technique. OSDD first divides the sample set into several subsets by using an improved stability criterion and K-means algorithm. We separately calculate the optimal discriminant vectors from each subset. Then we construct the projection transformation by combining the discriminant vectors derived from all subsets. Furthermore, we provide a nonlinear extension of OSDD, that is, the optimal subset-division based kernel discrimination (OSKD) approach. It employs the kernel K-means algorithm to divide the sample set in the kernel space and obtains the nonlinear projection transformation. The proposed approaches are applied to face and palmprint recognition, and are examined using the AR and FERET face databases and the PolyU palmprint database. The experimental results demonstrate that the proposed approaches outperform several related linear and nonlinear discriminant analysis methods.