Two-dimensional Laplacianfaces method for face recognition

Authors:
Ben Niu;Qiang Yang;Simon Chi Keung Shiu;Sankar Kumar Pal
Affiliations:
Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China;Department of Computing, Hong Kong Polytechnic University, Hong Kong, China;Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
Venue:
Pattern Recognition
Year:
2008

Citing 6
Cited 5

An optimal algorithm for approximate nearest neighbor searching fixed dimensions

Journal of the ACM (JACM)
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
Rapid and brief communication: Face recognition based on 2D Fisherface approach

Pattern Recognition
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

Two-dimensional local graph embedding discriminant analysis (2DLGEDA) with its application to face and palm biometrics

Neurocomputing
Gabor texture representation method for face recognition using the Gamma and generalized Gaussian models

Image and Vision Computing
Perfect histogram matching PCA for face recognition

Multidimensional Systems and Signal Processing
Sparse two-dimensional local discriminant projections for feature extraction

Neurocomputing
Improved discriminant locality preserving projections for face and palmprint recognition

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper we propose a two-dimensional (2D) Laplacianfaces method for face recognition. The new algorithm is developed based on two techniques, i.e., locality preserved embedding and image based projection. The 2D Laplacianfaces method is not only computationally more efficient but also more accurate than the one-dimensional (1D) Laplacianfaces method in extracting the facial features for human face authentication. Extensive experiments are performed to test and evaluate the new algorithm using the FERET and the AR face databases. The experimental results indicate that the 2D Laplacianfaces method significantly outperforms the existing 2D Eigenfaces, the 2D Fisherfaces and the 1D Laplacianfaces methods under various experimental conditions.