3,525 research outputs found
Demographic Bias in Presentation Attack Detection of Iris Recognition Systems
With the widespread use of biometric systems, the demographic bias problem
raises more attention. Although many studies addressed bias issues in biometric
verification, there are no works that analyze the bias in presentation attack
detection (PAD) decisions. Hence, we investigate and analyze the demographic
bias in iris PAD algorithms in this paper. To enable a clear discussion, we
adapt the notions of differential performance and differential outcome to the
PAD problem. We study the bias in iris PAD using three baselines (hand-crafted,
transfer-learning, and training from scratch) using the NDCLD-2013 database.
The experimental results point out that female users will be significantly less
protected by the PAD, in comparison to males.Comment: accepted for publication at EUSIPCO202
Fairness in Face Presentation Attack Detection
Face presentation attack detection (PAD) is critical to secure face
recognition (FR) applications from presentation attacks. FR performance has
been shown to be unfair to certain demographic and non-demographic groups.
However, the fairness of face PAD is an understudied issue, mainly due to the
lack of appropriately annotated data. To address this issue, this work first
presents a Combined Attribute Annotated PAD Dataset (CAAD-PAD) by combining
several well-known PAD datasets where we provide seven human-annotated
attribute labels. This work then comprehensively analyses the fairness of a set
of face PADs and its relation to the nature of training data and the
Operational Decision Threshold Assignment (ODTA) on different data groups by
studying four face PAD approaches on our CAAD-PAD. To simultaneously represent
both the PAD fairness and the absolute PAD performance, we introduce a novel
metric, namely the Accuracy Balanced Fairness (ABF). Extensive experiments on
CAAD-PAD show that the training data and ODTA induce unfairness on gender,
occlusion, and other attribute groups. Based on these analyses, we propose a
data augmentation method, FairSWAP, which aims to disrupt the identity/semantic
information and guide models to mine attack cues rather than attribute-related
information. Detailed experimental results demonstrate that FairSWAP generally
enhances both the PAD performance and the fairness of face PAD
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
The emergence of COVID-19 has had a global and profound impact, not only on
society as a whole, but also on the lives of individuals. Various prevention
measures were introduced around the world to limit the transmission of the
disease, including face masks, mandates for social distancing and regular
disinfection in public spaces, and the use of screening applications. These
developments also triggered the need for novel and improved computer vision
techniques capable of (i) providing support to the prevention measures through
an automated analysis of visual data, on the one hand, and (ii) facilitating
normal operation of existing vision-based services, such as biometric
authentication schemes, on the other. Especially important here, are computer
vision techniques that focus on the analysis of people and faces in visual data
and have been affected the most by the partial occlusions introduced by the
mandates for facial masks. Such computer vision based human analysis techniques
include face and face-mask detection approaches, face recognition techniques,
crowd counting solutions, age and expression estimation procedures, models for
detecting face-hand interactions and many others, and have seen considerable
attention over recent years. The goal of this survey is to provide an
introduction to the problems induced by COVID-19 into such research and to
present a comprehensive review of the work done in the computer vision based
human analysis field. Particular attention is paid to the impact of facial
masks on the performance of various methods and recent solutions to mitigate
this problem. Additionally, a detailed review of existing datasets useful for
the development and evaluation of methods for COVID-19 related applications is
also provided. Finally, to help advance the field further, a discussion on the
main open challenges and future research direction is given.Comment: Submitted to Image and Vision Computing, 44 pages, 7 figure
Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization
Current face recognition systems achieve high progress on several benchmark
tests. Despite this progress, recent works showed that these systems are
strongly biased against demographic sub-groups. Consequently, an easily
integrable solution is needed to reduce the discriminatory effect of these
biased systems. Previous work mainly focused on learning less biased face
representations, which comes at the cost of a strongly degraded overall
recognition performance. In this work, we propose a novel unsupervised fair
score normalization approach that is specifically designed to reduce the effect
of bias in face recognition and subsequently lead to a significant overall
performance boost. Our hypothesis is built on the notation of individual
fairness by designing a normalization approach that leads to treating similar
individuals similarly. Experiments were conducted on three publicly available
datasets captured under controlled and in-the-wild circumstances. Results
demonstrate that our solution reduces demographic biases, e.g. by up to 82.7%
in the case when gender is considered. Moreover, it mitigates the bias more
consistently than existing works. In contrast to previous works, our fair
normalization approach enhances the overall performance by up to 53.2% at false
match rate of 0.001 and up to 82.9% at a false match rate of 0.00001.
Additionally, it is easily integrable into existing recognition systems and not
limited to face biometrics.Comment: Accepted in Pattern Recognition Letter
Biometrics Institute 20th Anniversary Report
The purpose of this report is to mark the 20-year anniversary of the Biometrics Institute on the 11 October 2021. More importantly, however, this report celebrates the work of the Biometrics Institute over the past twenty years, which together with the support of its members, has provided a platform for a balanced discussion promoting the responsible and ethical use of biometrics and a deeper understanding of the biometrics industry
Pattern mining approaches used in sensor-based biometric recognition: a review
Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems
A Comprehensive Study on Face Recognition Biases Beyond Demographics
Face recognition (FR) systems have a growing effect on critical
decision-making processes. Recent works have shown that FR solutions show
strong performance differences based on the user's demographics. However, to
enable a trustworthy FR technology, it is essential to know the influence of an
extended range of facial attributes on FR beyond demographics. Therefore, in
this work, we analyse FR bias over a wide range of attributes. We investigate
the influence of 47 attributes on the verification performance of two popular
FR models. The experiments were performed on the publicly available MAADFace
attribute database with over 120M high-quality attribute annotations. To
prevent misleading statements about biased performances, we introduced control
group based validity values to decide if unbalanced test data causes the
performance differences. The results demonstrate that also many non-demographic
attributes strongly affect the recognition performance, such as accessories,
hair-styles and colors, face shapes, or facial anomalies. The observations of
this work show the strong need for further advances in making FR system more
robust, explainable, and fair. Moreover, our findings might help to a better
understanding of how FR networks work, to enhance the robustness of these
networks, and to develop more generalized bias-mitigating face recognition
solutions.Comment: Under review in IEEE Transactions on Technology and Societ
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