On the Biometric Capacity of Generative Face Models

• URL: http://arxiv.org/abs/2308.02065v1
• Date: Thu, 3 Aug 2023 22:21:04 GMT
• Title: On the Biometric Capacity of Generative Face Models
• Authors: Vishnu Naresh Boddeti and Gautam Sreekumar and Arun Ross
• Abstract summary: This paper proposes a statistical approach to estimate the biometric capacity of generated face images. We employ our approach on multiple generative models, including StyleGAN, Latent Diffusion Model, and "Generated Photos" 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 $1.43times106$ and $1.190times104$, respectively.
• Score: 23.66662504163745
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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