Being accurate, efficient, and compact is essential to a facial landmark detector for practical use. To simultaneously consider the three concerns, this paper investigates a neat model with promising detection accuracy under wild environments (e.g., unconstrained pose, expression, lighting, and occlusion conditions) and super real-time speed on a mobile device. More concretely, we customize an endto-end single stage network associated with acceleration techniques. During the training phase, for each sample, rotation information is estimated for geometrically regularizing landmark localization, which is then NOT involved in the testing phase. A novel loss is designed to, besides considering the geometrical regularization, mitigate the issue of data imbalance by adjusting weights of samples to different states, such as large pose, extreme lighting, and occlusion, in the training set. Extensive experiments are conducted to demonstrate the efficacy of our design and reveal its superior performance over state-ofthe-art alternatives on widely-adopted challenging benchmarks, i.e., 300W (including iBUG, LFPW, AFW, HELEN, and XM2VTS) and AFLW. Our model can be merely 2.1Mb of size and reach over 140 fps per face on a mobile phone(Qualcomm ARM 845 processor) with high precision, making it attractive for large-scale or real-time applications. We have made our practical system based on PFLD 0.25X model publicly available at http://sites.google. com/view/xjguo/fld for encouraging comparisons and improvements from the community.
| Backbone | Scale | param(K) | flop(G) | loss | config | pth | onnx | ncnn |
|---|---|---|---|---|---|---|---|---|
| MoboNetv2 | 112 | 140.55 | 0.0593 | 3.5e-5 | config | model | onnx | ncnn |
@article{guo2019PFLD,
title={PFLD: A Practical Facial Landmark Detector},
author={Xiaojie Guo, Siyuan Li, Jinke Yu, Jiawan Zhang, Jiayi Ma, Lin Ma, Wei Liu, and Haibin Ling},
journal={arXiv preprint arXiv:1902.10859,},
year={2019}
}