Proposal pyramid networks for fast face detection

Abstract

Recent face detectors based on deep convolutional neural networks (DCNN) have substantially improved the detection performance in the wild. However, the detection speed is still the biggest bottleneck, which hinders the practical deployment of these face detectors on resource-limited platforms. To address this issue, the paper proposes a Proposal Pyramid Network (PPN) to generate face candidates extremely fast, which reduces the major computational complexity in cascaded DCNN face detectors. PPN is a lightweight fully convolutional network with multiple branches, which takes a single image as input and generates face proposals with different scales in terms of separate branches simultaneously. In this manner, it avoids the traditional image pyramid structure and thus achieves a very fast processing speed. To evaluate the effectiveness of our proposed method, a three-stage cascaded DCNN face detector is realized based on PPN. Extensive experimental results on several public benchmarks show that the proposed face detector achieves comparable accuracy to the-state-of-arts while the detection speed reaches 60 FPS with an i5 CPU, which significantly exceeds previous DCNN-based face detectors. (The source code is available at https://github.com/zhaofan0622/PPN).