  Recently significant performance improvement in face detection was made
possible by deeply trained convolutional networks. In this report, a novel
approach for training state-of-the-art face detector is described. The key is
to exploit the idea of hard negative mining and iteratively update the Faster
R-CNN based face detector with the hard negatives harvested from a large set of
background examples. We demonstrate that our face detector outperforms
state-of-the-art detectors on the FDDB dataset, which is the de facto standard
for evaluating face detection algorithms.
