Face-recognition models trained on data rebalanced with a continuous ethnicity score are fairer, and often as accurate, as models trained on conventionally balanced data.
Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition
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abstract
This study investigates the effects of occlusions on the fairness of face recognition systems, particularly focusing on demographic biases. Using the Racial Faces in the Wild (RFW) dataset and synthetically added realistic occlusions, we evaluate their effect on the performance of face recognition models trained on the BUPT-Balanced and BUPT-GlobalFace datasets. We note increases in the dispersion of FMR, FNMR, and accuracy alongside decreases in fairness according to Equilized Odds, Demographic Parity, STD of Accuracy, and Fairness Discrepancy Rate. Additionally, we utilize a pixel attribution method to understand the importance of occlusions in model predictions, proposing a new metric, Face Occlusion Impact Ratio (FOIR), that quantifies the extent to which occlusions affect model performance across different demographic groups. Our results indicate that occlusions exacerbate existing demographic biases, with models placing higher importance on occlusions in an unequal fashion, particularly affecting African individuals more severely.
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Balancing Beyond Discrete Categories: Continuous Demographic Labels for Fair Face Recognition
Face-recognition models trained on data rebalanced with a continuous ethnicity score are fairer, and often as accurate, as models trained on conventionally balanced data.