G.D. Guo, Stan Z. Li, and HongJiang Zhang
We develop a pairwise classification framework for face recognition, in which a C class face recognition problem is divided into a set of C(C-1)/2 two class problems. Such a problem decomposition not only leads to a set of simpler classification problems to be solved, thereby increasing overall classification accuracy, but also provides a framework for independent feature selection for each pair of classes. A simple feature ranking strategy is used to select a small subset of features for each pair of classes. Furthermore, we evaluate two classification methods under the pairwise comparison framework, one is the Bayes classifier, and the other is AdaBoost, a large margin classifier. Experiments on a large face database with 1039 face images of 137 individuals indicate that 20 features are enough to achieve a relatively high recognition accuracy, which demonstrates the effectiveness of the pairwise recognition framework.