期刊
NEUROCOMPUTING
卷 299, 期 -, 页码 42-50出版社
ELSEVIER
DOI: 10.1016/j.neucom.2018.03.030
关键词
Face detection; Faster RCNN; Convolutional neural networks (CNN); Feature concatenation; Hard negative mining; Multi-scale training
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据