English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deeply-learned face representations enable the success of current facerecognition systems. Despite the ability of these representations to encode theidentity of an individual, recent works have shown that more information isstored within, such as demographics, image characteristics, and social traits.This threatens the user's privacy, since for many applications these templatesare expected to be solely used for recognition purposes. Knowing the encodedinformation in face templates helps to develop bias-mitigating andprivacy-preserving face recognition technologies. This work aims to support thedevelopment of these two branches by analysing face templates regarding 113attributes. Experiments were conducted on two publicly available faceembeddings. For evaluating the predictability of the attributes, we trained amassive attribute classifier that is additionally able to accurately state itsprediction confidence. This allows us to make more sophisticated statementsabout the attribute predictability. The results demonstrate that up to 74attributes can be accurately predicted from face templates. Especiallynon-permanent attributes, such as age, hairstyles, haircolors, beards, andvarious accessories, found to be easily-predictable. Since face recognitionsystems aim to be robust against these variations, future research might buildon this work to develop more understandable privacy preserving solutions andbuild robust and fair face templates.

Beyond Identity: What Information Is Stored in Biometric Face Templates?