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CORR
2008
Springer
159views Education» more  CORR 2008»
13 years 7 months ago
Face Detection Using Adaboosted SVM-Based Component Classifier
: Boosting is a general method for improving the accuracy of any given learning algorithm. In this paper we employ combination of Adaboost with Support Vector Machine (SVM) as comp...
Seyyed Majid Valiollahzadeh, Abolghasem Sayadiyan,...
ICPR
2006
IEEE
14 years 8 months ago
Enhancing Training Set for Face Detection
We present a novel method to enhance training set for face detection with nonlinearly generated examples from the original data. The motivation is from Support Vector Machines (SV...
Ruiping Wang, Jie Chen, Shiguang Shan, Wen Gao
AVBPA
2005
Springer
226views Biometrics» more  AVBPA 2005»
14 years 27 days ago
Discriminant Analysis Based on Kernelized Decision Boundary for Face Recognition
A novel nonlinear discriminant analysis method, Kernelized Decision Boundary Analysis (KDBA), is proposed in our paper, whose Decision Boundary feature vectors are the normal vecto...
Baochang Zhang, Xilin Chen, Wen Gao
ICCV
2007
IEEE
14 years 1 months ago
Support Kernel Machines for Object Recognition
Kernel classifiers based on Support Vector Machines (SVM) have recently achieved state-of-the art results on several popular datasets like Caltech or Pascal. This was possible by...
Ankita Kumar, Cristian Sminchisescu
MLDM
2005
Springer
14 years 26 days ago
An Automatic Face Recognition System in the Near Infrared Spectrum
Face recognition is a challenging visual classification task, especially when the lighting conditions can not be controlled. In this paper, we present an automatic face recognitio...
Shuyan Zhao, Rolf-Rainer Grigat