Many recognition algorithms depend on careful positioning of an object into a canonical pose, so the position of features relative to a fixed coordinate system can be examined. Cu...
Nearest neighbour classifiers and related kernel methods often perform poorly in high dimensional problems because it is infeasible to include enough training samples to cover the...
Abstract. Principal Component Analysis (PCA) plus Linear Discriminant Analysis (LDA) (PCA+LDA) and LDA/QR are both two-stage methods that deal with the small sample size (SSS) prob...
In many applications, such as credit default prediction and medical image recognition, test inputs are available in addition to the labeled training examples. We propose a method ...
A novel method for robust super-resolution offace images is proposed in this paper. Face super-resolution is a particular interest in video surveillance where face images have typ...