marginFace: A novel face recognition method by average neighborhood margin maximization

Authors:
Fei Wang;Xin Wang;Daoqiang Zhang;Changshui Zhang;Tao Li
Affiliations:
State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100 ...;School of Computing and Information Sciences, Florida International University, FL 33174, USA;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100 ...;School of Computing and Information Sciences, Florida International University, FL 33174, USA
Venue:
Pattern Recognition
Year:
2009

Citing 18
Cited 9

Introduction to statistical pattern recognition (2nd ed.)

Introduction to statistical pattern recognition (2nd ed.)
Local learning algorithms

Neural Computation
Visual learning and recognition of 3-D objects from appearance

International Journal of Computer Vision
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
Nonlinear component analysis as a kernel eigenvalue problem

Neural Computation
Fusion of LDA and PCA for Face Verification

ECCV '02 Proceedings of the International ECCV 2002 Workshop Copenhagen on Biometric Authentication
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods

FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
The CMU Pose, Illumination, and Expression Database

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
Out-of-core tensor approximation of multi-dimensional matrices of visual data

ACM SIGGRAPH 2005 Papers
Face Recognition by Stepwise Nonparametric Margin Maximum Criterion

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Generalized Low Rank Approximations of Matrices

Machine Learning
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Orthogonal Laplacianfaces for Face Recognition

IEEE Transactions on Image Processing
Multilinear Discriminant Analysis for Face Recognition

IEEE Transactions on Image Processing

A parameter-free locality sensitive discriminant analysis and its application to coarse 3D head pose estimation

ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
A supervised non-linear dimensionality reduction approach for manifold learning

Pattern Recognition
Enhanced fisher discriminant criterion for image recognition

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

Neural Processing Letters
Generalized local discriminant embedding for face recognition

ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
Feature extraction using two-dimensional neighborhood margin and variation embedding

Computer Vision and Image Understanding
Feature Extraction Based on Maximum Nearest Subspace Margin Criterion

Neural Processing Letters
Parameterless Local Discriminant Embedding

Neural Processing Letters
Sparse regularization discriminant analysis for face recognition

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

We propose a novel appearance-based face recognition method called the marginFace approach. By using average neighborhood margin maximization (ANMM), the face images are mapped into a face subspace for analysis. Different from principal component analysis (PCA) and linear discriminant analysis (LDA) which effectively see only the global Euclidean structure of face space, ANMM aims at discriminating face images of different people based on local information. More concretely, for each face image, it pulls the neighboring images of the same person towards it as near as possible, while simultaneously pushing the neighboring images of different people away from it as far as possible. Moreover, we propose an automatic approach for determining the optimal dimensionality of the embedded subspace. The kernelized (nonlinear) and tensorized (multilinear) form of ANMM are also derived in this paper. Finally the experimental results of applying marginFace to face recognition are presented to show the effectiveness of our method.