Orthogonal neighborhood preserving discriminant analysis for face recognition

Authors:
Haifeng Hu
Affiliations:
Department of Electronics and Communication Engineering, Sun Yat-sen University, Guangzhou 510275, PR China
Venue:
Pattern Recognition
Year:
2008

Citing 9
Cited 14

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
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
An Evaluation of Multimodal 2D+3D Face Biometrics

IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces for recognition

Journal of Cognitive Neuroscience
A novel incremental principal component analysis and its application for face recognition

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Bagging null space locality preserving discriminant classifiers for face recognition

Pattern Recognition
Direct kernel neighborhood discriminant analysis for face recognition

Pattern Recognition Letters
A doubly weighted approach for appearance-based subspace learning methods

IEEE Transactions on Information Forensics and Security
Face recognition using discriminant locality preserving projections based on maximum margin criterion

Pattern Recognition
Augmented DT-CWT feature based classification using Regularized Neighborhood Projection Discriminant Analysis for face recognition

Pattern Recognition
Orthogonal Tensor Neighborhood Preserving Embedding for facial expression recognition

Pattern Recognition
Locally Minimizing Embedding and Globally Maximizing Variance: Unsupervised Linear Difference Projection for Dimensionality Reduction

Neural Processing Letters
Orthogonal Complete Discriminant Locality Preserving Projections for Face Recognition

Neural Processing Letters
Supervised Discriminant Projection with Its Application to Face Recognition

Neural Processing Letters
Trace ratio criterion based generalized discriminative learning for semi-supervised dimensionality reduction

Pattern Recognition
Regularized locality preserving discriminant analysis for face recognition

Neurocomputing
Constrained large Margin Local Projection algorithms and extensions for multimodal dimensionality reduction

Pattern Recognition
Label propagation based supervised locality projection analysis for plant leaf classification

Pattern Recognition
Integrating iterative crossover capability in orthogonal neighborhoods for scheduling resource-constrained projects

Evolutionary Computation

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, we propose a new linear subspace analysis algorithm, called orthogonal neighborhood preserving discriminant analysis (ONPDA). Given a set of data points in the ambient space, a weight matrix is firstly built which describes the relationship between the data points. Then optimal between-class scatter matrix and within-class scatter matrix are defined such that the neighborhood structure can be preserved. In order to improve the discriminating power, a new method is presented for orthogonalizing the basis eigenvectors. We evaluate the performance of the proposed algorithm for face recognition with the use of different databases. Consistent and promising results demonstrate the effectiveness of our algorithm.