Validating Seed Data Samples for Synthetic Identities – Methodology and Uniqueness Metrics

by   Viktor Varkarakis, et al.

This work explores the identity attribute of synthetic face samples derived from Generative Adversarial Networks. The goal is to determine if individual samples are unique in terms of identity, firstly with respect to the seed dataset that trains the GAN model and secondly with respect to other synthetic face samples. Two approaches are introduced to enable the comparative analysis of large sets of synthetic face samples. The first of these uses ROC curves to determine identity uniqueness using a number of large publicly available datasets of real facial samples to provide reference ROCs as a baseline. The second approach uses a thresholding technique utilizing again large publicly available datasets as a reference. For this approach, new metrics are introduced, and a technique is provided to remove the most connected data samples within a large synthetic dataset. The remaining synthetic samples can be considered as unique as data samples gathered from different real individuals. Several StyleGAN models are used to create the synthetic datasets, and variations in key model parameters are explored. It is concluded that the resulting synthetic data samples exhibit excellent uniqueness when compared with the original training dataset, but significantly less uniqueness when comparisons are made within the synthetic dataset. Nevertheless, it is possible to remove the most highly connected synthetic data samples. Thus, in some cases, up to 92% of the data samples in a 20k synthetic dataset can be shown to exhibit similar uniqueness to data samples taken from real public datasets.


page 3

page 4

page 5

page 6

page 7

page 13

page 16

page 17


On the use of automatically generated synthetic image datasets for benchmarking face recognition

The availability of large-scale face datasets has been key in the progre...

Generating Photo-Realistic Training Data to Improve Face Recognition Accuracy

In this paper we investigate the feasibility of using synthetic data to ...

Re-Training StyleGAN – A First Step Towards Building Large, Scalable Synthetic Facial Datasets

StyleGAN is a state-of-art generative adversarial network architecture t...

The Florence 4D Facial Expression Dataset

Human facial expressions change dynamically, so their recognition / anal...

iWarpGAN: Disentangling Identity and Style to Generate Synthetic Iris Images

Generative Adversarial Networks (GANs) have shown success in approximati...

ChildGAN: Large Scale Synthetic Child Facial Data Using Domain Adaptation in StyleGAN

In this research work, we proposed a novel ChildGAN, a pair of GAN netwo...

Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification

In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework ...

Please sign up or login with your details

Forgot password? Click here to reset