WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation

arXiv:2606.31704v1 Announce Type: cross Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance disparities across demographic groups. A key obstacle to studying and mitigating such biases is the lack of face detection datasets with sensitive feature annotations. To address this gap, we introduce WIDER-FAIR, a new dataset built on the widely used WIDER-FACE benchmark, manually annotated with the perceived ethnicity and sex of each face. The dataset contains 16,256 images annotated across four eth

Source: arXiv cs.LG — read the full report at the original publisher.

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