Discriminant projection embedding for face and palmprint recognition

Authors:
Yan Yan;Yu-Jin Zhang
Affiliations:
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China and Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China;Department of Electronic Engineering, Tsinghua University, Beijing 100084, China and Tsinghua National Laboratory for Information Science and Technology, Tsinghua University, Beijing 100084, China
Venue:
Neurocomputing
Year:
2008

Citing 17
Cited 8

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory

The nature of statistical learning theory
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
Nonparametric discriminant analysis and nearest neighbor classification

Pattern Recognition Letters
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
A Unified Framework for Subspace Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Discriminant Analysis in Relevance Feedback for Content-Based Image Retrieval

ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Handbook of Face Recognition

Handbook of Face Recognition
Local Discriminant Embedding and Its Variants

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Neighborhood Preserving Embedding

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Equivalence of Some Common Linear Feature Extraction Techniques for Appearance-Based Object Recognition Tasks

IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised tensor learning

Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-LDA: A statistical linear discriminant analysis for image matrix

Pattern Recognition Letters

A novel class-dependence feature analysis method for face recognition

Pattern Recognition Letters
Outlier-resisting graph embedding

Neurocomputing
Orthogonal locally discriminant projection for palmprint recognition

ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Locally linear embedding: a survey

Artificial Intelligence Review
Face feature extraction and recognition based on discriminant subclass-center manifold preserving projection

Pattern Recognition Letters
Dynamic ROI extraction algorithm for palmprints

ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part II
Image classification based on effective extreme learning machine

Neurocomputing
An improved palmprint recognition system using iris features

Journal of Real-Time Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose a new supervised linear dimensionality reduction method called discriminant projection embedding (DPE). DPE can preserve within-class neighboring geometry and extract between-class relevant structures for classification effectively. The proposed method is applied to face and palmprint recognition and is examined using the AR and FERET face databases and the PolyU palmprint database. Experimental results show that DPE consistently outperforms other up-to-date supervised linear dimensionality reduction methods when the training sample size per class is small. This demonstrates the effectiveness and robustness of DPE.