A two-stage linear discriminant analysis for face-recognition

Authors:
Alok Sharma;Kuldip K. Paliwal
Affiliations:
Signal Processing Lab, Griffith University, Australia and School of Engineering & Physics, University of the South Pacific, Fiji and Laboratory of DNA Information Analysis, Human Genome Center, In ...;Signal Processing Lab, Griffith University, Australia
Venue:
Pattern Recognition Letters
Year:
2012

Citing 17
Cited 0

Using Discriminant Eigenfeatures for Image Retrieval

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
Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class

IEEE Transactions on Pattern Analysis and Machine Intelligence
Solving the Small Sample Size Problem of LDA

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Regularized discriminant analysis for the small sample size problem in face recognition

Pattern Recognition Letters
Characterization of a Family of Algorithms for Generalized Discriminant Analysis on Undersampled Problems

The Journal of Machine Learning Research
Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

Pattern Recognition Letters
A Maximum Uncertainty LDA-Based Approach for Limited Sample Size Problems — With Application to Face Recognition

SIBGRAPI '05 Proceedings of the XVIII Brazilian Symposium on Computer Graphics and Image Processing
Class-dependent PCA, MDC and LDA: A combined classifier for pattern classification

Pattern Recognition
Rapid and brief communication: Why direct LDA is not equivalent to LDA

Pattern Recognition
Eigenfeature Regularization and Extraction in Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotational Linear Discriminant Analysis Technique for Dimensionality Reduction

IEEE Transactions on Knowledge and Data Engineering
Prediction of eigenvalues and regularization of eigenfeatures for human face verification

Pattern Recognition Letters
Improved direct LDA and its application to DNA microarray gene expression data

Pattern Recognition Letters
A new perspective to null linear discriminant analysis method and its fast implementation using random matrix multiplication with scatter matrices

Pattern Recognition

Quantified Score

Hi-index	0.10

Visualization

Abstract

A two-stage linear discriminant analysis technique is proposed that utilizes both the null space and range space information of scatter matrices. The technique regularizes both the between-class scatter and within-class scatter matrices to extract the discriminant information. The regularization is conducted in parallel to give two orientation matrices. These orientation matrices are concatenated to form the final orientation matrix. The proposed technique is shown to provide better classification performance on face recognition datasets than the other techniques.