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ICPR
2004
IEEE
16 years 5 months ago
Support Vector Machine with Local Summation Kernel for Robust Face Recognition
This paper presents Support Vector Machine (SVM) with local summation kernel for robust face recognition. In recent years, the effectiveness of SVM and local features is reported....
Kazuhiro Hotta
ICB
2009
Springer
140views Biometrics» more  ICB 2009»
15 years 11 months ago
A Discriminant Analysis Method for Face Recognition in Heteroscedastic Distributions
Linear discriminant analysis (LDA) is a popular method in pattern recognition and is equivalent to Bayesian method when the sample distributions of different classes are obey to t...
Zhen Lei, ShengCai Liao, Dong Yi, Rui Qin, Stan Z....
FGR
2006
IEEE
117views Biometrics» more  FGR 2006»
15 years 10 months ago
A Landmark Paper in Face Recognition
Good registration (alignment to a reference) is essential for accurate face recognition. The effects of the number of landmarks on the mean localization error and the recognition ...
G. M. Beumer, Qian Tao, Asker M. Bazen, Raymond N....
ICIP
2005
IEEE
15 years 10 months ago
View independent face recognition based on kernel principal component analysis of local parts
This paper presents a view independent face recognition method based on kernel principal component analysis (KPCA) of local parts. View changes induce large variation in feature s...
Koji Hotta
SCIA
2009
Springer
195views Image Analysis» more  SCIA 2009»
15 years 9 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...