Pairwise Face Recognition

We develop a pairwise classification framework for face recognition, in which a class face recognition problem is divided into a set of ? ?? ? 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 pair 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 ?? features are enough to achieve a relatively high recognition accuracy, which demonstrates the effectiveness of the pairwise recognition framework.

• Type: Conference
• Year: 2001
• Where: ICCV
• Authors: Guodong Guo, HongJiang Zhang, Stan Z. Li
• Keywords: Computer Vision, Face Recognition Problem, ICCV 2001, Overall Classification Accuracy, Pairwise Classification Framework, Pairwise Comparison Framework, Pairwise Recognition Framework