  To improve the discriminative and generalization ability of lightweight
network for face recognition, we propose an efficient variable group
convolutional network called VarGFaceNet. Variable group convolution is
introduced by VarGNet to solve the conflict between small computational cost
and the unbalance of computational intensity inside a block. We employ variable
group convolution to design our network which can support large scale face
identification while reduce computational cost and parameters. Specifically, we
use a head setting to reserve essential information at the start of the network
and propose a particular embedding setting to reduce parameters of
fully-connected layer for embedding. To enhance interpretation ability, we
employ an equivalence of angular distillation loss to guide our lightweight
network and we apply recursive knowledge distillation to relieve the
discrepancy between the teacher model and the student model. The champion of
deepglint-light track of LFR (2019) challenge demonstrates the effectiveness of
our model and approach. Implementation of VarGFaceNet will be released at
https://github.com/zma-c-137/VarGFaceNet soon.
