  State-of-the-art face recognition networks are often computationally
expensive and cannot be used for mobile applications. Training lightweight face
recognition models also requires large identity-labeled datasets. Meanwhile,
there are privacy and ethical concerns with collecting and using large face
recognition datasets. While generating synthetic datasets for training face
recognition models is an alternative option, it is challenging to generate
synthetic data with sufficient intra-class variations. In addition, there is
still a considerable gap between the performance of models trained on real and
synthetic data. In this paper, we propose a new framework (named SynthDistill)
to train lightweight face recognition models by distilling the knowledge of a
pretrained teacher face recognition model using synthetic data. We use a
pretrained face generator network to generate synthetic face images and use the
synthesized images to learn a lightweight student network. We use synthetic
face images without identity labels, mitigating the problems in the intra-class
variation generation of synthetic datasets. Instead, we propose a novel dynamic
sampling strategy from the intermediate latent space of the face generator
network to include new variations of the challenging images while further
exploring new face images in the training batch. The results on five different
face recognition datasets demonstrate the superiority of our lightweight model
compared to models trained on previous synthetic datasets, achieving a
verification accuracy of 99.52% on the LFW dataset with a lightweight network.
The results also show that our proposed framework significantly reduces the gap
between training with real and synthetic data. The source code for replicating
the experiments is publicly released.

Computer Vision and Pattern Recognition