Detecting Face with Densely Connected Face Proposal Network | |
Zhang, Shifeng1,2; Zhu, Xiangyu1,2; Lei, Zhen1,2; Shi, Hailin1,2; Wang, Xiaobo1,2; Li, Stan Z.1,2 | |
2017 | |
会议日期 | 2017-10 |
会议地点 | 中国深圳 |
英文摘要 | Accuracy and efficiency are two conflicting challenges for face detection, since effective models tend to be computationally prohibitive. To address these two conflicting challenges, our core idea is to shrink the input image and focus on detecting small faces. Specifically, we propose a novel face detector, dubbed the name Densely Connected Face Proposal Network (DCFPN), with high performance as well as real-time speed on the CPU devices. On the one hand, we subtly design a lightweight-butpowerful fully convolutional network with the consideration of efficiency and accuracy. On the other hand, we use the dense anchor strategy and propose a fair L1 loss function to handle small faces well. As a consequence, our method can detect faces at 30 FPS on a single 2.60 GHz CPU core and 250 FPS using a GPU for the VGA-resolution images. We achieve state-of-the-art performance on the AFW, PASCAL face and FDDB datasets. |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/39049] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_生物识别与安全技术研究中心 |
通讯作者 | Lei, Zhen |
作者单位 | 1.Institute of Automation Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Zhang, Shifeng,Zhu, Xiangyu,Lei, Zhen,et al. Detecting Face with Densely Connected Face Proposal Network[C]. 见:. 中国深圳. 2017-10. |
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