Lights, Camera, Matching: The Role of Image Illumination in Fair Face Recognition
This work addresses fairness issues in face recognition systems for demographic groups, though it is incremental as it builds on existing brightness adjustment techniques.
The paper tackled the problem of fairness in face recognition by addressing accuracy gaps due to facial brightness differences across demographic groups, specifically reducing the d' gap between Caucasian and African American female mated image pairs by up to 57.6% through brightness balancing methods.
Facial brightness is a key image quality factor impacting face recognition accuracy differentials across demographic groups. In this work, we aim to decrease the accuracy gap between the similarity score distributions for Caucasian and African American female mated image pairs, as measured by d' between distributions. To balance brightness across demographic groups, we conduct three experiments, interpreting brightness in the face skin region either as median pixel value or as the distribution of pixel values. Balancing based on median brightness alone yields up to a 46.8% decrease in d', while balancing based on brightness distribution yields up to a 57.6% decrease. In all three cases, the similarity scores of the individual distributions improve, with mean scores maximally improving 5.9% for Caucasian females and 3.7% for African American females.