We present a class of extremely efficient CNN models, MobileFaceNets, which
use less than 1 million parameters and are specifically tailored for
high-accuracy real-time face verification on mobile and embedded devices. We
first make a simple analysis on the weakness of common mobile networks for face
verification. The weakness has been well overcome by our specifically designed
MobileFaceNets. Under the same experimental conditions, our MobileFaceNets
achieve significantly superior accuracy as well as more than 2 times actual
speedup over MobileNetV2. After trained by ArcFace loss on the refined
MS-Celeb-1M, our single MobileFaceNet of 4.0MB size achieves 99.55% accuracy on
LFW and 92.59% TAR@FAR1e-6 on MegaFace, which is even comparable to
state-of-the-art big CNN models of hundreds MB size. The fastest one of
MobileFaceNets has an actual inference time of 18 milliseconds on a mobile
phone. For face verification, MobileFaceNets achieve significantly improved
efficiency over previous state-of-the-art mobile CNNs.

本文提出了 MobileFaceNets 类的 CNN 模型，其使用不到 100 万个参数，专为移动和嵌入式设备上的高准确度实时人脸验证而设计，并成功解决了普通移动网络在人脸验证方面的弱点，达到了与数百 MB 大小的最新大型 CNN 模型相媲美的高精度和更多超过 MobileNetV2 2 倍的实际加速。