Face detection, as a fundamental technology for various applications, is
always deployed on edge devices which have limited memory storage and low
computing power. This paper introduces a Light and Fast Face Detector (LFFD)
for edge devices. The proposed method is anchor-free and belongs to the
one-stage category. Specifically, we rethink the importance of receptive field
(RF) and effective receptive field (ERF) in the background of face detection.
Essentially, the RFs of neurons in a certain layer are distributed regularly in
the input image and theses RFs are natural "anchors". Combining RF "anchors"
and appropriate RF strides, the proposed method can detect a large range of
continuous face scales with 100% coverage in theory. The insightful
understanding of relations between ERF and face scales motivates an efficient
backbone for one-stage detection. The backbone is characterized by eight
detection branches and common layers, resulting in efficient computation.
Comprehensive and extensive experiments on popular benchmarks: WIDER FACE and
FDDB are conducted. A new evaluation schema is proposed for
application-oriented scenarios. Under the new schema, the proposed method can
achieve superior accuracy (WIDER FACE Val/Test -- Easy: 0.910/0.896, Medium:
0.881/0.865, Hard: 0.780/0.770; FDDB -- discontinuous: 0.973, continuous:
0.724). Multiple hardware platforms are introduced to evaluate the running
efficiency. The proposed method can obtain fast inference speed (NVIDIA TITAN
Xp: 131.45 FPS at 640x480; NVIDIA TX2: 136.99 PFS at 160x120; Raspberry Pi 3
Model B+: 8.44 FPS at 160x120) with model size of 9 MB.

本文提出了一种针对边缘设备的轻量级和高速人脸检测器，其方法是基于无锚点的一阶段检测，通过重新考虑感受野和有效感受野（ERF）之间的关系来实现多尺度的连续人脸检测。综合实验结果表明，该方法在多个数据集上取得了更高的准确度和更快的推断速度。