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...
: This communication describes a representation of images as a set of edges characterized by their position and orientation. This representation allows the comparison of two images...
Subspacefacerecognitionoftensuffersfromtwoproblems:(1)thetrainingsamplesetissmallcompared with the high dimensional feature vector; (2) the performance is sensitive to the subspace...
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
At present, there are many methods for frontal view face recognition. However, few of them can work well when only one example image per class is available. In this paper, we pres...