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arXiv · 1608.02236

Bootstrapping Face Detection with Hard Negative Examples

Shaohua Wan·Zhijun Chen·Tao Zhang·Bo Zhang·Kong-kat Wong
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Abstract

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.

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Shaohua Wan, Zhijun Chen, Tao Zhang, Bo Zhang, Kong-kat Wong. 2016-08-07. Bootstrapping Face Detection with Hard Negative Examples. https://arxiv.org/abs/1608.02236

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