: 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...
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...
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...
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...
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...