3 research outputs found
Beyond Identity: What Information Is Stored in Biometric Face Templates?
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 accurately state its
prediction confidence. This allows us to make more sophisticated statements
about the attribute predictability. The results demonstrate that up to 74
attributes can be accurately predicted from face templates. Especially
non-permanent attributes, such as age, hairstyles, haircolors, beards, and
various accessories, found to be easily-predictable. Since face recognition
systems aim to be robust against these variations, future research might build
on this work to develop more understandable privacy preserving solutions and
build robust and fair face templates.Comment: To appear in IJCB 202
Four dimensions characterize comprehensive trait judgments of faces
People readily attribute many traits to faces: some look beautiful, some competent, some aggressive. These snap judgments have important consequences in real life, ranging from success in political elections to decisions in courtroom sentencing. Modern psychological theories argue that the hundreds of different words people use to describe others from their faces are well captured by only two or three dimensions, such as valence and dominance, a highly influential framework that has been the basis for numerous studies in social and developmental psychology, social neuroscience, and in engineering applications. However, all prior work has used only a small number of words (12 to 18) to derive underlying dimensions, limiting conclusions to date. Here we employed deep neural networks to select a comprehensive set of 100 words that are representative of the trait words people use to describe faces, and to select a set of 100 faces. In two large-scale, preregistered studies we asked participants to rate the 100 faces on the 100 words (obtaining 2,850,000 ratings from 1,710 participants), and discovered a novel set of four psychological dimensions that best explain trait judgments of faces: warmth, competence, femininity, and youth. We reproduced these four dimensions across different regions around the world, in both aggregated and individual-level data. These results provide a new and most comprehensive characterization of face judgments, and reconcile prior work on face perception with work in social cognition and personality psychology