On the Biometric Capacity of Generative Face Models
Vishnu Naresh Boddeti Gautam Sreekumar Arun Ross
There has been tremendous progress in generating realistic faces with high
fidelity over the past few years. Despite this progress, a crucial question
remains unanswered: "Given a generative face model, how many unique identities
can it generate?" In other words, what is the biometric capacity of the
generative face model? A scientific basis for answering this question will
benefit evaluating and comparing different generative face models and establish
an upper bound on their scalability. This paper proposes a statistical approach
to estimate the biometric capacity of generated face images in a hyperspherical
feature space. We employ our approach on multiple generative models, including
unconditional generators like StyleGAN, Latent Diffusion Model, and "Generated
Photos," as well as DCFace, a class-conditional generator. We also estimate
capacity w.r.t. demographic attributes such as gender and age. Our capacity
estimates indicate that (a) under ArcFace representation at a false acceptance
rate (FAR) of 0.1%, StyleGAN3 and DCFace have a capacity upper bound of
$1.43\times10^6$ and $1.190\times10^4$, respectively; (b) the capacity reduces
drastically as we lower the desired FAR with an estimate of $1.796\times10^4$
and $562$ at FAR of 1% and 10%, respectively, for StyleGAN3; (c) there is no
discernible disparity in the capacity w.r.t gender; and (d) for some generative
models, there is an appreciable disparity in the capacity w.r.t age. Code is
available at https://github.com/human-analysis/capacity-generative-face-models.