Face detection is frequently attempted by using heavy pre-trained backbone
networks like ResNet-50/101/152 and VGG16/19. Few recent works have also
proposed lightweight detectors with customized backbones, novel loss functions
and efficient training strategies. The novelty of this work lies in the design
of a lightweight detector while training with only the commonly used loss
functions and learning strategies. The proposed face detector grossly follows
the established RetinaFace architecture. The first contribution of this work is
the design of a customized lightweight backbone network (BLite) having 0.167M
parameters with 0.52 GFLOPs. The second contribution is the use of two
independent multi-task losses. The proposed lightweight face detector (FDLite)
has 0.26M parameters with 0.94 GFLOPs. The network is trained on the WIDER FACE
dataset. FDLite is observed to achieve 92.3\%, 89.8\%, and 82.2\% Average
Precision (AP) on the easy, medium, and hard subsets of the WIDER FACE
validation dataset, respectively.

本文提出了一种定制的轻量级背骨网络 (BLite) 和一种轻量级人脸检测器 (FDLite)，采用常用的损失函数和学习策略进行训练，并在 WIDER FACE 验证数据集的不同子集上分别达到了 92.3%、89.8% 和 82.2% 的平均准确率 (AP)。