Finding Regions of Uncertainty in Learned Models: An Application to Face Detection

After training statistical models to classify sets of data into predetermined classes, it is often di cult to interpret what the models have learned. This paper presents a novel approach for nding examples which lie on the decision boundaries of statistical models trained for classi cation. These examples provide insight into what the model has learned. Additionally, they can provide candidates for use as additional training data for improving the performance of the statistical models. By labeling the examples which lie on the decision boundaries, we provide information to the model in the regions in which it is most uncertain. The approaches presented in this paper are demonstrated on the real-world vision-based task of detecting faces in cluttered scenes.