14 research outputs found
SER-FIQ: Unsupervised Estimation of Face Image Quality Based on Stochastic Embedding Robustness
Face image quality is an important factor to enable high performance face
recognition systems. Face quality assessment aims at estimating the suitability
of a face image for recognition. Previous work proposed supervised solutions
that require artificially or human labelled quality values. However, both
labelling mechanisms are error-prone as they do not rely on a clear definition
of quality and may not know the best characteristics for the utilized face
recognition system. Avoiding the use of inaccurate quality labels, we proposed
a novel concept to measure face quality based on an arbitrary face recognition
model. By determining the embedding variations generated from random
subnetworks of a face model, the robustness of a sample representation and
thus, its quality is estimated. The experiments are conducted in a
cross-database evaluation setting on three publicly available databases. We
compare our proposed solution on two face embeddings against six
state-of-the-art approaches from academia and industry. The results show that
our unsupervised solution outperforms all other approaches in the majority of
the investigated scenarios. In contrast to previous works, the proposed
solution shows a stable performance over all scenarios. Utilizing the deployed
face recognition model for our face quality assessment methodology avoids the
training phase completely and further outperforms all baseline approaches by a
large margin. Our solution can be easily integrated into current face
recognition systems and can be modified to other tasks beyond face recognition.Comment: Accepted at CVPR202
Face Quality Estimation and Its Correlation to Demographic and Non-Demographic Bias in Face Recognition
Face quality assessment aims at estimating the utility of a face image for
the purpose of recognition. It is a key factor to achieve high face recognition
performances. Currently, the high performance of these face recognition systems
come with the cost of a strong bias against demographic and non-demographic
sub-groups. Recent work has shown that face quality assessment algorithms
should adapt to the deployed face recognition system, in order to achieve
highly accurate and robust quality estimations. However, this could lead to a
bias transfer towards the face quality assessment leading to discriminatory
effects e.g. during enrolment. In this work, we present an in-depth analysis of
the correlation between bias in face recognition and face quality assessment.
Experiments were conducted on two publicly available datasets captured under
controlled and uncontrolled circumstances with two popular face embeddings. We
evaluated four state-of-the-art solutions for face quality assessment towards
biases to pose, ethnicity, and age. The experiments showed that the face
quality assessment solutions assign significantly lower quality values towards
subgroups affected by the recognition bias demonstrating that these approaches
are biased as well. This raises ethical questions towards fairness and
discrimination which future works have to address.Comment: Accepted at IJCB202
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