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AMFG
2005
IEEE
314views Biometrics» more  AMFG 2005»
15 years 3 months ago
Two-Dimensional Non-negative Matrix Factorization for Face Representation and Recognition
Non-negative matrix factorization (NMF) is a recently developed method for finding parts-based representation of non-negative data such as face images. Although it has successfully...
Daoqiang Zhang, Songcan Chen, Zhi-Hua Zhou
CVPR
2005
IEEE
15 years 3 months ago
Nonlinear Face Recognition Based on Maximum Average Margin Criterion
This paper proposes a novel nonlinear discriminant analysis method named by Kernerlized Maximum Average Margin Criterion (KMAMC), which has combined the idea of Support Vector Mac...
Baochang Zhang, Xilin Chen, Shiguang Shan, Wen Gao
ICCV
2011
IEEE
13 years 9 months ago
Sparse Representation or Collaborative Representation: Which Helps Face Recognition?
As a recently proposed technique, sparse representation based classification (SRC) has been widely used for face recognition (FR). SRC first codes a testing sample as a sparse lin...
Lei Zhang, Meng Yang, Xiangchu Feng
CVPR
2008
IEEE
15 years 4 months ago
Unified Principal Component Analysis with generalized Covariance Matrix for face recognition
Recently, 2DPCA and its variants have attracted much attention in face recognition area. In this paper, some efforts are made to discover the underlying fundaments of these method...
Shiguang Shan, Bo Cao, Yu Su, Laiyun Qing, Xilin C...
SCIA
2009
Springer
195views Image Analysis» more  SCIA 2009»
15 years 2 months ago
Multi-band Gradient Component Pattern (MGCP): A New Statistical Feature for Face Recognition
A feature extraction method using multi-frequency bands is proposed for face recognition, named as the Multi-band Gradient Component Pattern (MGCP). The MGCP captures discriminativ...
Yimo Guo, Jie Chen, Guoying Zhao, Matti Pietik&aum...