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» An Improvement on PCA Algorithm for Face Recognition
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PAMI
2008
200views more  PAMI 2008»
13 years 7 months ago
Principal Component Analysis Based on L1-Norm Maximization
In data-analysis problems with a large number of dimension, principal component analysis based on L2-norm (L2PCA) is one of the most popular methods, but L2-PCA is sensitive to out...
Nojun Kwak
CVPR
2004
IEEE
13 years 11 months ago
Face Localization via Hierarchical CONDENSATION with Fisher Boosting Feature Selection
We formulate face localization as a Maximum A Posteriori Probability(MAP) problem of finding the best estimation of human face configuration in a given image. The a prior distribu...
Jilin Tu, ZhenQiu Zhang, Zhihong Zeng, Thomas S. H...
ICIP
2005
IEEE
14 years 1 months ago
Robust face alignment based on local texture classifiers
We propose a robust face alignment algorithm with a novel discriminative local texture model. Different from the conventional descriptive PCA local texture model in ASM, classifie...
Li Zhang, Haizhou Ai, Shengjun Xin, Chang Huang, S...
FGR
2006
IEEE
255views Biometrics» more  FGR 2006»
13 years 11 months ago
Incremental Kernel SVD for Face Recognition with Image Sets
Non-linear subspaces derived using kernel methods have been found to be superior compared to linear subspaces in modeling or classification tasks of several visual phenomena. Such...
Tat-Jun Chin, Konrad Schindler, David Suter
DICTA
2003
13 years 8 months ago
Face Recognition Based on Multiple Region Features
For face recognition, face feature selection is an important step. Better features should result in better performance. This paper describes a robust face recognition algorithm usi...
Jiaming Li, Geoff Poulton, Ying Guo, Rong-yu Qiao