Feature extraction based on Laplacian bidirectional maximum margin criterion

Authors:
Wankou Yang;Jianguo Wang;Mingwu Ren;Jingyu Yang;Lei Zhang;Guanghai Liu
Affiliations:
School of Computer Science & Technology, Nanjing University of Science and Technology, Nanjing 210094, China;Network & Education Center, Tangshan College, Tangshan 063000, China;School of Computer Science & Technology, Nanjing University of Science and Technology, Nanjing 210094, China;School of Computer Science & Technology, Nanjing University of Science and Technology, Nanjing 210094, China;Dept. of Computing, The Hong Kong Polytechnic University, Hong Kong;School Computer Science and Information Technology, Guangxi Normal University, Guangxi, China
Venue:
Pattern Recognition
Year:
2009

Citing 33
Cited 9

Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal discriminant plane for a small number of samples and design method of classifier on the plane

Pattern Recognition
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners

IEEE Transactions on Pattern Analysis and Machine Intelligence
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
PCA versus LDA

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

Neural Computation
Learning a Locality Preserving Subspace for Visual Recognition

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing faces with PCA and ICA

Computer Vision and Image Understanding - Special issue on Face recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Embedding: A General Framework for Dimensionality Reduction

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Face recognition based on 2D Fisherface approach

Pattern Recognition
Improved discriminate analysis for high-dimensional data and its application to face 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
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
1D-LDA vs. 2D-LDA: When is vector-based linear discriminant analysis better than matrix-based?

Pattern Recognition
Correlation Metric for Generalized Feature Extraction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential row-column independent component analysis for face recognition

Neurocomputing
A convergent solution to tensor subspace learning

IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Face recognition using discriminant locality preserving projections

Image and Vision Computing
Rapid and brief communication: Two-dimensional FLD for face recognition

Pattern Recognition
Rapid and brief communication: Two-dimensional discriminant transform for face recognition

Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix

Pattern Recognition Letters
Bidirectional PCA with assembled matrix distance metric for image recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
BDPCA plus LDA: a novel fast feature extraction technique for face recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Effective Feature Extraction in High-Dimensional Space

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Image Classification Using Correlation Tensor Analysis

IEEE Transactions on Image Processing
Efficient and robust feature extraction by maximum margin criterion

IEEE Transactions on Neural Networks

On the relevance of linear discriminative features

Information Sciences: an International Journal
A structure-preserved local matching approach for face recognition

Pattern Recognition Letters
Face Recognition Using Kernel UDP

Neural Processing Letters
Deriving implicit indoor scene structure with path analysis

Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness
Enhanced eigenspace separation transform for classification

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Complete large margin linear discriminant analysis using mathematical programming approach

Pattern Recognition
Two-dimensional color uncorrelated discriminant analysis for face recognition

Neurocomputing
Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction

Pattern Recognition
Kernel inverse Fisher discriminant analysis for face recognition

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

Maximum margin criterion (MMC) based feature extraction is more efficient than linear discriminant analysis (LDA) for calculating the discriminant vectors since it does not need to calculate the inverse within-class scatter matrix. However, MMC ignores the discriminative information within the local structures of samples and the structural information embedding in the images. In this paper, we develop a novel criterion, namely Laplacian bidirectional maximum margin criterion (LBMMC), to address the issue. We formulate the image total Laplacian matrix, image within-class Laplacian matrix and image between-class Laplacian matrix using the sample similar weight that is widely used in machine learning. The proposed LBMMC based feature extraction computes the discriminant vectors by maximizing the difference between image between-class Laplacian matrix and image within-class Laplacian matrix in both row and column directions. Experiments on the FERET and Yale face databases show the effectiveness of the proposed LBMMC based feature extraction method.