We develop a pairwise classification framework for face recognition, in which a class face recognition problem is divided into a set of ´ ½µ ¾ 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 the features for each

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... air of classes. Furthermore, we evaluate two classification methods under the pairwise comparison framework: the Bayes classifier and the AdaBoost. Experiments on a large face database with 1079 face images of 137 individuals indicate that ¾¼ features are enough to achieve a relatively high recognition accuracy, which demonstrates the effectiveness of the pairwise recognition framework.