Complete local Fisher discriminant analysis with Laplacian score ranking for face recognition

Authors:
Hong Huang;Hailiang Feng;Chengyu Peng
Affiliations:
Key Laboratory on Opto-Electronic Technique and Systems, Ministry of Education, Chongqing University, 400044 Chongqing, China;Key Laboratory on Opto-Electronic Technique and Systems, Ministry of Education, Chongqing University, 400044 Chongqing, China;Key Laboratory on Opto-Electronic Technique and Systems, Ministry of Education, Chongqing University, 400044 Chongqing, China
Venue:
Neurocomputing
Year:
2012

Citing 20
Cited 0

From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose

IEEE Transactions on Pattern Analysis and Machine Intelligence
Laplacian Eigenmaps for dimensionality reduction and data representation

Neural Computation
The CMU Pose, Illumination, and Expression Database

IEEE Transactions on Pattern Analysis and Machine Intelligence
KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment

SIAM Journal on Scientific Computing
Face Recognition Using Laplacianfaces

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

The Journal of Machine Learning Research
Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Applications to Face and Palm Biometrics

IEEE Transactions on Pattern Analysis and Machine Intelligence
SRDA: An Efficient Algorithm for Large-Scale Discriminant Analysis

IEEE Transactions on Knowledge and Data Engineering
Complete discriminant evaluation and feature extraction in kernel space for face recognition

Machine Vision and Applications
Locality sensitive discriminant analysis

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A complete fuzzy discriminant analysis approach for face recognition

Applied Soft Computing
Complete neighborhood preserving embedding for face recognition

Pattern Recognition
Letters: Laplacian bidirectional PCA for face recognition

Neurocomputing
Local margin based semi-supervised discriminant embedding for visual recognition

Neurocomputing
Enhanced semi-supervised local Fisher discriminant analysis for face recognition

Future Generation Computer Systems
Ear recognition based on uncorrelated local Fisher discriminant analysis

Neurocomputing
A linear subspace learning approach via sparse coding

ICCV '11 Proceedings of the 2011 International Conference on Computer Vision

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose a novel manifold learning method, called complete local Fisher discriminant analysis (CLFDA), for face recognition. LFDA often suffers from the small sample size problem, which makes the local within-class scatter matrix singular. In practice, principal component analysis is applied as a preprocessing step to solve this problem. However, this strategy may discard dimensions that contain important discriminative information. The aim of CLFDA is to make full use of two kinds of discriminant information, regular and irregular. At first, CLFDA removes the null space of local within-class scatter to extract the regular discriminant features in the range space of local within-class scatter. Then, the irregular discriminant features are extracted in the null space of local between-class scatter. In addition, we impose Laplacian score to rank the regular and irregular features to achieve the best performance more quickly. Experiments on AT&T, YaleB and CMU PIE face databases are performed to demonstrate the effectiveness of the proposed method.