A Comparative Study on Synthetic Facial Data Generation Techniques for Face Recognition

TL;DR

This paper compares various synthetic facial data generation techniques, such as diffusion models, GANs, and 3D models, assessing their effectiveness in improving face recognition accuracy and robustness while addressing privacy and bias issues.

Contribution

It provides a comprehensive comparative analysis of synthetic facial data generation methods and their impact on face recognition performance across multiple datasets.

Findings

Synthetic data can effectively capture realistic facial variations.

Diffusion models, GANs, and 3D models show significant progress.

Performance gaps with real data still exist.

Abstract

Facial recognition has become a widely used method for authentication and identification, with applications for secure access and locating missing persons. Its success is largely attributed to deep learning, which leverages large datasets and effective loss functions to learn discriminative features. Despite these advances, facial recognition still faces challenges in explainability, demographic bias, privacy, and robustness to aging, pose variations, lighting changes, occlusions, and facial expressions. Privacy regulations have also led to the degradation of several datasets, raising legal, ethical, and privacy concerns. Synthetic facial data generation has been proposed as a promising solution. It mitigates privacy issues, enables experimentation with controlled facial attributes, alleviates demographic bias, and provides supplementary data to improve models trained on real data. This study compares the effectiveness of synthetic facial datasets generated using different techniques in facial recognition tasks. We evaluate accuracy, rank-1, rank-5, and the true positive rate at a false positive rate of 0.01% on eight leading datasets, offering a comparative analysis not extensively explored in the literature. Results demonstrate the ability of synthetic data to capture realistic variations while emphasizing the need for further research to close the performance gap with real data. Techniques such as diffusion models, GANs, and 3D models show substantial progress; however, challenges remain.