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Recently significant performance improvement in face detection was madepossible by deeply trained convolutional networks. In this report, a novelapproach for training state-of-the-art face detector is described. The key isto exploit the idea of hard negative mining and iteratively update the FasterR-CNN based face detector with the hard negatives harvested from a large set ofbackground examples. We demonstrate that our face detector outperformsstate-of-the-art detectors on the FDDB dataset, which is the de facto standardfor evaluating face detection algorithms.

Bootstrapping Face Detection with Hard Negative Examples