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Face Recognition Using Laplacianfaces
March 2005 (vol. 27 no. 3)
pp. 328-340
ASCII Text
x 
 
Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hong-Jiang Zhang, "Face Recognition Using Laplacianfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, March, 2005.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2005.55,
author = {Xiaofei He and Shuicheng Yan and Yuxiao Hu and Partha Niyogi and Hong-Jiang Zhang},
title = {Face Recognition Using Laplacianfaces},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {27},
number = {3},
issn = {0162-8828},
year = {2005},
pages = {328-340},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2005.55},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Face Recognition Using Laplacianfaces
IS - 3
SN - 0162-8828
SP328
EP340
EPD - 328-340
A1 - Xiaofei He,
A1 - Shuicheng Yan,
A1 - Yuxiao Hu,
A1 - Partha Niyogi,
A1 - Hong-Jiang Zhang,
PY - 2005
KW - Face recognition
KW - principal component analysis
KW - linear discriminant analysis
KW - locality preserving projections
KW - face manifold
KW - subspace learning.
VL - 27
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
We propose an appearance-based face recognition method called the Laplacianface approach. By using Locality Preserving Projections (LPP), 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 Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition.

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Index Terms:
Face recognition, principal component analysis, linear discriminant analysis, locality preserving projections, face manifold, subspace learning.
Citation:
Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, Hong-Jiang Zhang, "Face Recognition Using Laplacianfaces," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, March 2005, doi:10.1109/TPAMI.2005.55
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