  Enabling highly secure applications (such as border crossing) with face
recognition requires extensive biometric performance tests through large scale
data. However, using real face images raises concerns about privacy as the laws
do not allow the images to be used for other purposes than originally intended.
Using representative and subsets of face data can also lead to unwanted
demographic biases and cause an imbalance in datasets. One possible solution to
overcome these issues is to replace real face images with synthetically
generated samples. While generating synthetic images has benefited from recent
advancements in computer vision, generating multiple samples of the same
synthetic identity resembling real-world variations is still unaddressed, i.e.,
mated samples. This work proposes a non-deterministic method for generating
mated face images by exploiting the well-structured latent space of StyleGAN.
Mated samples are generated by manipulating latent vectors, and more precisely,
we exploit Principal Component Analysis (PCA) to define semantically meaningful
directions in the latent space and control the similarity between the original
and the mated samples using a pre-trained face recognition system. We create a
new dataset of synthetic face images (SymFace) consisting of 77,034 samples
including 25,919 synthetic IDs. Through our analysis using well-established
face image quality metrics, we demonstrate the differences in the biometric
quality of synthetic samples mimicking characteristics of real biometric data.
The analysis and results thereof indicate the use of synthetic samples created
using the proposed approach as a viable alternative to replacing real biometric
data.
