Detection and Mitigation of Bias in Under Exposure Estimation for Face Image Quality Assessment
André Dörsch, Christian Rathgeb, Marcel Grimmer, Christoph Busch · 2024
The increasing employment of large scale biometric systems such as the European “Entry-Exit System” and planned national initiatives such as the “Live Enrolment” procedure require quality assessment algorithms to ensure reliable recognition accuracy. Among other factors, facial image quality and hence face recognition accuracy can be negatively impacted by underexposure. Therefore, quality assessment algorithms analyse the exposure of live-captured facial images. To this end, mainly handcrafted measures have been proposed which are also referenced in current standards. However, this work shows that handcrafted measures, which use basic statistical approaches to analyse facial brightness patterns, exhibit racial bias. It is found that these algorithms disproportionately classify images of black people as underexposed as they do not take into account natural differences in skin color, particularly when relying on average pixel brightness values. To ensure fair biometric quality assessment, we have fine-tuned a data-efficient image transformer (DeiT) on synthetic data. The resulting underexposure estimation outperforms state-of-the-art algorithms in detection accuracy and biometric fairness. Precisely, an Equal Error Rate (EER) of approximately 7% is achieved. Our findings highlight the importance of developing robust and fair biometric classification methods to mitigate discrimination and ensure fair performance for all users, regardless of their skin color.