ICPR 2022 - Workshop Fairness in
August 21, 2022
Workshop on Fairness in Biometric Systems
In recent years, biometric systems spread worldwide and are increasingly involved in critical decision-making processes, such as in finance, public security, and forensics. Despite their growing effect on everybody’s daily life, many biometric solutions perform strongly different on different groups of individuals as previous works have shown. Consequently, the recognition performances of these systems are strongly dependent on demographic and non-demographic attributes of their users. This results in discriminatory and unfair treatment of the user of these systems.
However, several political regulations point out the importance of the right to non-discrimination. These include Article 14 of the European Convention of Human Rights, Article 7 of the Universal Declaration of Human Rights, and Article 71 of the General Data Protection Regulation (GDPR). These political efforts show the strong need for analyzing and mitigating these equability concerns in biometric systems.
Current works on this topic are focused on demographic-fairness in face recognition systems. However, since there is a growing effect on everybody’s daily life and an increased social interest in this topic, research on fairness in biometric solutions is urgently needed.
Developing and analyzing biometric datasets.
Proposing metric related to equability in biometrics.
Demographic and non-demographic factors in biometric systems.
Investigating and mitigating equability concerns in biometric algorithms including
Identity verification and identification
Soft-biometric attribute estimation
Presentation attack detection
Biometric image generation
Topics (not limited to):
Datasets designed for the evaluation and development of
fair biometric solutions.
Demographic and non-demographic fairness concerns
Differential performance and outcome in biometric systems.
Estimation of equability in biometric systems.
Explainability and transparency in biometrics.
Explainability-aware and equability–mitigating
Evaluating and mitigating equability-issues in biometric solutions, including identity recognition, soft-biometric attribute estimation, presentation attack detection, and quality assessment.
Yevgeniy B. Sirotin (Maryland Test Facility / IDSL / SAIC )
Demographic Differentials in Face Recognition Systems: Applied Research Challenges
Ignacio Serna (Autonomous University of Madrid / California Institute of Technology)
Where are we on measuring bias?