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by Junping Zhang, Stan Z. Li, Jue Wang
6th IEEE International Conference on Automatic Face and Gesture Recognition
http://research.microsoft.com/%7Eszli/papers/ZJP-FG2004.pdf
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Abstract:
Faces under varying illumination, pose and non-rigid deformation are empirically thought of as a highly nonlinear manifold in the observation space. How to discover intrinsic low-dimensional manifold is important to characterize meaningful face distributions and classify them using a simpler, such as linear or Gaussian based, classifier. In this paper, we present a manifold learning algorithm (MLA) for learning a mapping from highly-dimensional manifold into the intrinsic low-dimensional linear manifold. We also propose the nearest manifold (NM) criterion for the classification and present an algorithm for computing the distance from the sample to be classified to the nearest face manifolds in light of local linearity of manifold. Based on these works, face recognition is achieved with the combination of MLA and NM. Experiments on several face databases show that the advantages of our proposed combinational approach. 1
Citations
|
1745
|
Eigenfaces for Recognition
– Turk, Pentland
- 1991
|
|
628
|
A Global Geometric Framework for Nonlinear Dimensionality
– Tanenbaum, Silva, et al.
- 2000
|
|
580
|
Nonlinear dimensionality reduction by locally linear embedding
– Roweis, Saul
- 2000
|
|
271
|
Detecting face in images: A survey
– Yang, Kriegman, et al.
- 2002
|
|
203
|
GTM: The generative topographic mapping
– Bishop, Svensén, et al.
- 1998
|
|
182
|
Automatic Recognition and Analysis of Human Faces and Facial Expressions: A
– Samal
- 1992
|
|
107
|
Face recognition using hidden Markov models
– Samaria
- 1994
|
|
38
|
Stochastic neighbor embedding
– Hinton, Roweis
|
|
24
|
and Partha Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation
– Belkin
- 2003
|
|
17
|
Vapnik: Statistical Learning Theory
– N
- 1998
|
|
10
|
Performance evaluation of the nearest feature line method in image classification and retrieval
– Li, Chan, et al.
- 2000
|
|
7
|
Akamatsu “Automatic Classification of Single Facial Images
– Lyons, Budynek
- 1999
|
|
2
|
Charting a manifold,” Neural Information Proceeding Systems: Natural and Synthetic
– Brand, MERL
|
|
2
|
Huang (eds), ”em Characterizing virtual Eigensignatures for General Purpose Face Recognition”, Daniel B Graham and Nigel M Allinson
– Wechsler, Phillips, et al.
- 1998
|
|
1
|
Akamatsu,”How Should We Represent Faces for Automatic
– Craw, Costen, et al.
|
|
1
|
Face Recognition,” in Biometrics: Personal Identification in Networked Society
– Weng, Swets
- 1999
|
