SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data

TL;DR

SynthDistill introduces a novel framework for training lightweight face recognition models by distilling knowledge from a pretrained teacher using synthetic data, effectively addressing privacy concerns and data scarcity.

Contribution

The paper presents a new synthetic data-based knowledge distillation framework that improves lightweight face recognition models without requiring identity labels.

Findings

Achieved 99.52% verification accuracy on LFW.

Reduced performance gap between real and synthetic data training.

Outperformed previous synthetic dataset-based models.

Abstract

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.