Short Communication: A novel local preserving projection scheme for use with face recognition

Authors:
Yong Xu;Fengxi Song;Ge Feng;Yingnan Zhao
Affiliations:
Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guang Dong 518055, China;New Star Research Institute of Applied Technology, Hefei, Anhui 230031, China;Harbin Institute of Technology, Shenzhen Graduate School, Shenzhen, Guang Dong 518055, China;Nanjing University of Information Science and Technology, Nanjing, Jiangsu 210044, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 12
Cited 6

Learning a Locality Preserving Subspace for Visual Recognition

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical and computational analysis of locality preserving projection

ICML '05 Proceedings of the 22nd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition

Pattern Recognition
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast communication: Gabor feature-based face recognition using supervised locality preserving projection

Signal Processing
A Direct Locality Preserving Projections (DLPP) Algorithm for Image Recognition

Neural Processing Letters
Palmprint recognition with improved two-dimensional locality preserving projections

Image and Vision Computing
Down-Sampling Face Images and Low-Resolution Face Recognition

ICICIC '08 Proceedings of the 2008 3rd International Conference on Innovative Computing Information and Control
A New Solution Scheme of Unsupervised Locality Preserving Projection Method for the SSS Problem

SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Face recognition using discriminant locality preserving projections

Image and Vision Computing

LPP solution schemes for use with face recognition

Pattern Recognition
ProClusEnsem: Predicting membrane protein types by fusing different modes of pseudo amino acid composition

Computers in Biology and Medicine
Local similarity and diversity preserving discriminant projection for face and handwriting digits recognition

Neurocomputing
Fisher Difference Discriminant Analysis: Determining the Effective Discriminant Subspace Dimensions for Face Recognition

Neural Processing Letters
Orthogonal discriminant vector for face recognition across pose

Pattern Recognition
Face recognition by using overlapping block discriminative common vectors

IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering

Quantified Score

Hi-index	12.05

Visualization

Abstract

When locality preserving projection (LPP) is applied to face recognition, it usually suffers from the small sample size (SSS) problem, which means that the eigen-equation of LPP cannot be solved directly. In order to address this issue, we propose a novel LPP scheme. This scheme transforms the objective function of LPP into a new function, which allows the resultant eigen-equation to be directly solved no matter whether the SSS problem occurs or not. Moreover, the fact that the proposed scheme has an adjustable parameter enables us to be able to obtain the best classification accuracy by adjusting the parameter. Our analysis comprehensively reveals the theoretical properties of the proposed scheme and its relationship with other LPP methods. Our analysis also shows that the conventional LPP can be regarded as a special form of the proposed scheme, which also implies that the classification accuracy of the conventional LPP will be lower than the best classification accuracy of the proposed scheme.