Beyond Identity: What Information Is Stored in Biometric Face Templates?
Philipp Terh\"orst, Daniel F\"ahrmann, Naser Damer, Florian, Kirchbuchner, Arjan Kuijper

TL;DR
This paper analyzes what information, including demographic and social traits, is stored in deep face templates, revealing that many attributes can be accurately predicted, raising privacy concerns.
Contribution
It provides a comprehensive analysis of 113 attributes stored in face templates, highlighting the extent of information leakage and its implications for privacy and fairness.
Findings
Up to 74 attributes can be accurately predicted from face templates.
Non-permanent attributes like age and hairstyle are especially predictable.
The attribute classifier also estimates prediction confidence for better analysis.
Abstract
Deeply-learned face representations enable the success of current face recognition systems. Despite the ability of these representations to encode the identity of an individual, recent works have shown that more information is stored within, such as demographics, image characteristics, and social traits. This threatens the user's privacy, since for many applications these templates are expected to be solely used for recognition purposes. Knowing the encoded information in face templates helps to develop bias-mitigating and privacy-preserving face recognition technologies. This work aims to support the development of these two branches by analysing face templates regarding 113 attributes. Experiments were conducted on two publicly available face embeddings. For evaluating the predictability of the attributes, we trained a massive attribute classifier that is additionally able to…
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