Recent studies witnessed that deep CNNs significantly improve the performance of face detection in the wild. However, detecting faces with small scales, large pose variations, and occlusions is still challenging. In this paper, to detect challenging faces, we present a boosted faster RCNN (F-RCN) version with an enhanced region proposal network (eRPN) module and newly introduced hard example mining strategies. The eRPN module generates better proposals than traditional RPN by integarating semantic information into the input feature maps. Two hard example mining strategies, i.e., online hard proposal mining (OHPM) and offline hard image mining (OHIM), are proposed to train better classifier. The OHPM can effectively sample quality and diversity of hard positive examples, which is important for detecting hard faces like tiny faces. The OHIM further boosts the classifier to detect hard faces via an auxiliary fine-tuning on a small proportion of training data. Experimental results on the FDDB, WIDER FACE, Pascal Faces, and AFW datasets show that our method significantly improves the faster-RCNN face detector and achieves performance superior or comparable to the state-of-the-art face detectors.

中文翻译：

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用更好的建议和分类器查找困难面孔

最近的研究表明，深层的CNN可以显着提高野外人脸检测的性能。然而，以小比例，大姿势变化和遮挡检测面部仍然是挑战。在本文中，为了检测具有挑战性的面孔，我们提出了增强的快速RCNN（F-RCN）版本，增强的区域提议网络（eRPN）模块和新引入的硬示例挖掘策略。通过将语义信息整合到输入特征图中，eRPN模块产生比传统RPN更好的建议。提出了两种硬性示例挖掘策略，即在线硬性提议挖掘（OHPM）和离线硬性图像挖掘（OHIM），以训练更好的分类器。OHPM可以有效地采样硬正样本的质量和多样性，这对于检测硬脸（如小脸）非常重要。OHIM进一步促进了分类器通过一小部分训练数据的辅助微调来检测硬脸。在FDDB，WIDER FACE，Pascal Faces和AFW数据集上的实验结果表明，我们的方法显着改善了快速RCNN面部检测器，并获得了与最新的面部检测器相比更好或相当的性能。