A feature extraction approach based on typical samples and its application to face recognition

Authors:
Yong Xu;Fengxi Song
Affiliations:
Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China
Venue:
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Year:
2007

Citing 10
Cited 0

Optimal discriminant plane for a small number of samples and design method of classifier on the plane

Pattern Recognition
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
Nonparametric discriminant analysis and nearest neighbor classification

Pattern Recognition Letters
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA

Pattern Recognition Letters
A fast kernel-based nonlinear discriminant analysis for multi-class problems

Pattern Recognition
Subspace distance analysis with application to adaptive Bayesian algorithm for face recognition

Pattern Recognition
Linear dimensionality reduction using relevance weighted LDA

Pattern Recognition
Robust coding schemes for indexing and retrieval from large face databases

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

To overcome shortcomings of traditional linear discriminant analysis such as failing to extract features of data with complex distributions, a new LDA approach is proposed in this paper. This approach is based on the following perspective: for a sample, the sample that is from the same class and is the farthest away from this sample, is typical intra-class sample of this sample. On the other hand, for the same sample, the nearest neighbor from each of other classes is called typical inter-class sample. In practice "typical samples" of a sample have indicative meaning for the space relation between this sample and the others. The new LDA approach bases definitions of between-class and within-class scatter matrices on these typical samples. As a result, the linear transform associated with our approach is able to maximize the distances between a sample and the corresponding typical inter-class samples, while minimizing the distance between the same sample and the typical intra-class sample. The proposed new approach is able to extract features of not only data with simple distributions but also the data with complex distributions, which means that the new LDA approach has wider applicability than traditional LDA.