4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CV2025
Mitigating Bias in Facial Recognition Systems: Centroid Fairness Loss Optimization
Jean-Rémy Conti, Stéphan Clémençon
The urging societal demand for fair AI systems has put pressure on the research community to develop predictive models that are not only globally accurate but also meet new fairnes…
cs.CV2022
Assessing Uncertainty in Similarity Scoring: Performance & Fairness in Face Recognition
Jean-Rémy Conti, Stéphan Clémençon
The ROC curve is the major tool for assessing not only the performance but also the fairness properties of a similarity scoring function. In order to draw reliable conclusions base…
cs.CV2022★ 4 cited
Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Model
Jean-Rémy Conti, Nathan Noiry, Vincent Despiegel +2
In spite of the high performance and reliability of deep learning algorithms in a wide range of everyday applications, many investigations tend to show that a lot of models exhibit…