Detecting Face with Densely Connected Face Proposal Network

Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, Stan Z. Li

Abstract: 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-but-powerful 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.