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2017 | OriginalPaper | Chapter

Detecting Face with Densely Connected Face Proposal Network

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

Published in: Biometric Recognition

Publisher: Springer International Publishing

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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.

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Metadata
Title
Detecting Face with Densely Connected Face Proposal Network
Authors
Shifeng Zhang
Xiangyu Zhu
Zhen Lei
Hailin Shi
Xiaobo Wang
Stan Z. Li
Copyright Year
2017
Book
Biometric Recognition

Print ISBN: 978-3-319-69922-6

Electronic ISBN: 978-3-319-69923-3

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-69923-3_1

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