In this paper, we present a set of extremely efficient and high throughput
models for accurate face verification, MixFaceNets which are inspired by Mixed
Depthwise Convolutional Kernels. Extensive experiment evaluations on Label Face
in the Wild (LFW), Age-DB, MegaFace, and IARPA Janus Benchmarks IJB-B and IJB-C
datasets have shown the effectiveness of our MixFaceNets for applications
requiring extremely low computational complexity. Under the same level of
computation complexity (< 500M FLOPs), our MixFaceNets outperform
MobileFaceNets on all the evaluated datasets, achieving 99.60% accuracy on LFW,
97.05% accuracy on AgeDB-30, 93.60 TAR (at FAR1e-6) on MegaFace, 90.94 TAR (at
FAR1e-4) on IJB-B and 93.08 TAR (at FAR1e-4) on IJB-C. With computational
complexity between 500M and 1G FLOPs, our MixFaceNets achieved results
comparable to the top-ranked models, while using significantly fewer FLOPs and
less computation overhead, which proves the practical value of our proposed
MixFaceNets. All training codes, pre-trained models, and training logs have
been made available this https URL

针对面部验证应用所需要效率极高和吞吐量大的需求，本文提出了基于混合深度卷积核的一组极其高效的深度学习模型 MixFaceNets，在多个数据集中对模型进行了评估，结果表明在计算复杂度相同的情况下，MixFaceNets 在所有的数据集上性能优于 MobileFaceNets，同时在计算复杂性在 500M 和 1G FLOPs 之间时，我们 MixFaceNets 的结果与排名前几位的模型相当，同时使用的 FLOPs 更少，验证了我们提出的 MixFaceNets 的实用价值。