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Recently, different researchers have found that the gallery composition of a face database can induce performance differentials to facial identification systems in which a probe image is compared against up to all stored reference images to reach a biometric decision. This negative effect is referred to as watchlist imbalance effect. In this work, we present a method to theoretically estimate said effect for a biometric identification system given its verification performance across demographic groups and the composition of the used gallery. Further, we report results for identification experiments on differently composed demographic subsets, i.e. females and males, of the public academic MORPH database using the open-source ArcFace face recognition system. It is shown that the database composition has a huge impact on performance differentials in biometric identification systems, even if performance differentials are less pronounced in the verification scenario. This study represents the first detailed analysis of the watchlist imbalance effect which is expected to be of high interest for future research in the field of facial recognition.
We propose a discrimination-aware learning method to improve both accuracy and fairness of biased face recognition algorithms. The most popular face recognition benchmarks assume a distribution of subjects without paying much attention to their demog
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The proliferation of automated facial recognition in various commercial and government sectors has caused significant privacy concerns for individuals. A recent and popular approach to address these privacy concerns is to employ evasion attacks again