Combining Classifiers in Rotated Face Space

Face recognition is a very complex classification problem due to nuisance variations in different conditions. Normally no single classifier can discriminate patterns well when unpredictable variations and a huge number of classes are involved. Combining multiple classifiers can improve discriminability over the best single classifier. In this paper, we present a way to combine classifiers for face recognition problem based on APCA classifiers. The proposed combinator generates various classifiers by rotating various face spaces and fusing them by applying a weighted distance measure. The combined classifier is tested on the Asian Face Database with 856 images. Experiments show a 30% reduction in classification error rate of our combined classifier and illustrates that combining classifiers from different face spaces may perform better than those based on a single face space.