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         author = {Kotwal, Ketan and Marcel, S{\'{e}}bastien},
       keywords = {bias mitigation, Demographic bias, Fairness, regularization},
       projects = {Idiap},
          month = jan,
          title = {Mitigating Demographic Bias in Face Recognition via Regularized Score Calibration},
      booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision Workshops},
           year = {2024},
      publisher = {IEEE/CVF},
       abstract = {Demographic bias in deep learning-based face recognition systems has led to serious concerns. Several existing works attempt to mitigate bias by incorporating demographic-specific processing during inference, which requires knowledge or learning of demographic attribute with an  additional cost. We propose to regularize training of the face recognition CNN, for demographic fairness, by imposing constraints on the distributions of matching scores. Our regularization term enforces the score distributions from different demographic groups to respect a predefined probability distribution, as well as it penalizes misalignment of distributions across demographic groups. The proposed method improves fairness of face recognition models without compromising the recognition accuracy, and does not require extra resources during inference. Our experiments indicate that in a cross-dataset testing, the regularized CNN can reduce the variation in accuracies (i.e., more fairness) of different demographic groups up to 25\% while slightly improving recognition accuracy over baselines.},
            pdf = {https://publications.idiap.ch/attachments/papers/2023/Kotwal_WACV-W_2024.pdf}