FaceBoxes: A CPU real-time face detector with high accuracy 论文

摘要

Although tremendous strides have been made in face detection, one of the remaining open challenges is to achieve real-time speed on the CPU as well as maintain high performance, since effective models for face detection tend to be computationally prohibitive.To address this challenge, we propose a novel face detector, named FaceBoxes, with superior performance on both speed and accuracy.Specifically, our method has a lightweight yet powerful network structure that consists of the Rapidly Digested Convolutional Layers (RDCL) and the Multiple Scale Convolutional Layers (MSCL).The RDCL is designed to enable Face-Boxes to achieve real-time speed on the CPU.The MSCL aims at enriching the receptive fields and discretizing anchors over different layers to handle faces of various scales.Besides, we propose a new anchor densification strategy to make different types of anchors have the same density on the image, which significantly improves the recall rate of small faces.As a consequence, the proposed detector runs at 20 FPS on a single CPU core and 125 FPS using a GPU for VGA-resolution images.Moreover, the speed of FaceBoxes is invariant to the number of faces.We comprehensively evaluate this method and present stateof-the-art detection performance on several face detection benchmark datasets, including the AFW, PASCAL face, and FDDB.