Actively Exploring Creation of Face Space(s) for Improved Face Recognition

摘要

We propose a learning framework that actively explores creation of face space(s) by selecting images that are complementary to the images already represented in the face space. We also construct ensembles of classifiers learned from such actively sampled image sets, which further provides improvement in the recognition rates. We not only significantly reduce the number of images required in the training set but also improve the accuracy over learning from all the images. We also show that the single face space or ensemble of face spaces, thus constructed, has a higher generalization performance across different illumination and expression conditions.