Facial analysis systems have been deployed by large companies and critiqued
by scholars and activists for the past decade. Many existing algorithmic audits
examine the performance of these systems on later stage elements of facial
analysis systems like facial recognition and age, emotion, or perceived gender
prediction; however, a core component to these systems has been vastly
understudied from a fairness perspective: face detection, sometimes called face
localization. Since face detection is a pre-requisite step in facial analysis
systems, the bias we observe in face detection will flow downstream to the
other components like facial recognition and emotion prediction. Additionally,
no prior work has focused on the robustness of these systems under various
perturbations and corruptions, which leaves open the question of how various
people are impacted by these phenomena. We present the first of its kind
detailed benchmark of face detection systems, specifically examining the
robustness to noise of commercial and academic models. We use both standard and
recently released academic facial datasets to quantitatively analyze trends in
face detection robustness. Across all the datasets and systems, we generally
find that photos of individuals who are $\textit{masculine presenting}$,
$\textit{older}$, of $\textit{darker skin type}$, or have $\textit{dim
lighting}$ are more susceptible to errors than their counterparts in other
identities.

这篇研究论文旨在通过详细的基准测试来检查商业和学术模型的人脸检测问题，特别关注这些系统对于不同身份的人的鲁棒性和公平性的问题，结果发现西方男性、年龄偏大、皮肤较暗和光照条件较差的人更容易受到错误的影响。

人脸检测中的鲁棒性差异

Robustness Disparities in Face Detection

Building facial analysis systems that generalize to extreme variations in
lighting and facial expressions is a challenging problem that can potentially
be alleviated using natural-looking synthetic data. Towards that, we propose
LEGAN, a novel synthesis framework that leverages perceptual quality judgments
for jointly manipulating lighting and expressions in face images, without
requiring paired training data. LEGAN disentangles the lighting and expression
subspaces and performs transformations in the feature space before upscaling to
the desired output image. The fidelity of the synthetic image is further
refined by integrating a perceptual quality estimation model, trained with face
images rendered using multiple synthesis methods and their crowd-sourced
naturalness ratings, into the LEGAN framework as an auxiliary discriminator.
Using objective metrics like FID and LPIPS, LEGAN is shown to generate higher
quality face images when compared with popular GAN models like StarGAN and
StarGAN-v2 for lighting and expression synthesis. We also conduct a perceptual
study using images synthesized by LEGAN and other GAN models and show the
correlation between our quality estimation and visual fidelity. Finally, we
demonstrate the effectiveness of LEGAN as training data augmenter for
expression recognition and face verification tasks.

提出了 LEGAN 框架来处理在光照和表情方面极端变化的面部图像合成。该框架使用感知质量判断来处理灯光和表情子空间，并在向量转换到图像时进行提高精度的操作。通过使用客观指标 FID 和 LPIPS 以及主观方式来证明 LEGAN 与 StarGAN 和 StarGAN-v2 相比，其生成的面部图像具有更高的质量，并可用于表情识别和面部验证任务的训练数据扩充。