Bias-Aware Face Mask Detection Dataset

TL;DR

This paper introduces a new, bias-aware face mask detection dataset with diverse racial and age representation, improving model fairness and generalization in face mask detection during the COVID-19 pandemic.

Contribution

The authors present the BAFMD dataset, addressing racial, gender, and age biases in existing datasets, and demonstrate its effectiveness in enhancing model performance.

Findings

BAFMD dataset reduces racial and age bias in face mask detection models.

Models trained on BAFMD outperform those trained on previous datasets.

The dataset improves generalization across diverse demographic groups.

Abstract

In December 2019, a novel coronavirus (COVID-19) spread so quickly around the world that many countries had to set mandatory face mask rules in public areas to reduce the transmission of the virus. To monitor public adherence, researchers aimed to rapidly develop efficient systems that can detect faces with masks automatically. However, the lack of representative and novel datasets proved to be the biggest challenge. Early attempts to collect face mask datasets did not account for potential race, gender, and age biases. Therefore, the resulting models show inherent biases toward specific race groups, such as Asian or Caucasian. In this work, we present a novel face mask detection dataset that contains images posted on Twitter during the pandemic from around the world. Unlike previous datasets, the proposed Bias-Aware Face Mask Detection (BAFMD) dataset contains more images from underrepresented race and age groups to mitigate the problem for the face mask detection task. We perform experiments to investigate potential biases in widely used face mask detection datasets and illustrate that the BAFMD dataset yields models with better performance and generalization ability. The dataset is publicly available at https://github.com/Alpkant/BAFMD.