Face detection using deep learning: An improved faster RCNN approach
Face detection using deep learning: An improved faster RCNN approach
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DOI：
10.1016/j.neucom.2018.03.030
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发表时间：
2018-07-19
期刊：
影响因子：
6
通讯作者：
Hoi, Steven C. H.
中科院分区：
文献类型：
--
作者：
Sun, Xudong;Wu, Pengcheng;Hoi, Steven C. H.
在本文中，我们提出了一种新的基于深度学习的人脸检测方案，并在著名的FDDB人脸检测基准评估上实现了最优检测性能。特别是，我们通过结合多种策略来改进最先进的Faster RCNN框架，包括特征拼接、硬负挖掘、多尺度训练、模型预训练和关键参数的适当校准。因此，该方案获得了最先进的人脸检测性能，并在FDDB基准上被评为已发表方法的ROC曲线最佳模型之一。
In this paper, we present a new face detection scheme using deep learning and achieve the state-of-theart detection performance on the well-known FDDB face detection benchmark evaluation. In particular, we improve the state-of-the-art Faster RCNN framework by combining a number of strategies, including feature concatenation, hard negative mining, multi-scale training, model pre-training, and proper calibration of key parameters. As a consequence, the proposed scheme obtained the state-of-the-art face detection performance and was ranked as one of the best models in terms of ROC curves of the published methods on the FDDB benchmark.