If It's Not Enough, Make It So: Reducing Authentic Data Demand in Face Recognition through Synthetic Faces

TL;DR

This paper investigates using synthetic face data to reduce reliance on authentic data for training face recognition models, addressing privacy concerns and data scarcity.

Contribution

It demonstrates how synthetic data, combined with authentic data and augmentation, can effectively train face recognition models, reducing the need for large authentic datasets.

Findings

Synthetic data can complement authentic data to improve recognition accuracy.

Data augmentation enhances model performance with limited authentic data.

Combining synthetic and authentic data yields better results than using either alone.

Abstract

Recent advances in deep face recognition have spurred a growing demand for large, diverse, and manually annotated face datasets. Acquiring authentic, high-quality data for face recognition has proven to be a challenge, primarily due to privacy concerns. Large face datasets are primarily sourced from web-based images, lacking explicit user consent. In this paper, we examine whether and how synthetic face data can be used to train effective face recognition models with reduced reliance on authentic images, thereby mitigating data collection concerns. First, we explored the performance gap among recent state-of-the-art face recognition models, trained with synthetic data only and authentic (scarce) data only. Then, we deepened our analysis by training a state-of-the-art backbone with various combinations of synthetic and authentic data, gaining insights into optimizing the limited use of the latter for verification accuracy. Finally, we assessed the effectiveness of data augmentation approaches on synthetic and authentic data, with the same goal in mind. Our results highlighted the effectiveness of FR trained on combined datasets, particularly when combined with appropriate augmentation techniques.