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CSSD: Cascade Single Shot Face Detector
Wang Shuainan 1,Xu Tong 2,Li Wei 2,Sun Haifeng 2 *
1.State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications;State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications
2.
*Correspondence author
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Funding: none
Opened online:25 January 2019
Accepted by: none
Citation: Wang Shuainan,Xu Tong,Li Wei.CSSD: Cascade Single Shot Face Detector[OL]. [25 January 2019] http://en.paper.edu.cn/en_releasepaper/content/4747093
 
 
Face detection has achieved great success with the development of convolution neural network. However, it remains a challenging problem to detect small and blurred faces in unconstrained environment. This paper presents a novel cascade single-shot face detector, named Cascade Single Shot Face Detector (CSSD), which introduces novel cascade classification and regression network in an anchor-based face detector to reject false positives and improve location accuracy. We have contributed in the following three aspects: 1) proposing a feature enchanted and scale-invariable face detection architecture to process faces with different scales; 2) regressing bounding boxes of faces in two steps with a cascade method; 3) filtering negative anchors online after anchor refinement and rebalancing puzzle negative anchors and positive anchors with rate of 3:1. As a consequence, our method achieves state-of-the-art detection performance on FDDB and WIDER FACE dataset.
Keywords:one-stage; cascade regression; face detection; deep convolutional neural network
 
 
 

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