Facial detection and analysis systems have been deployed by large companies
and critiqued by scholars and activists for the past decade. Critiques that
focus on system performance analyze disparity of the system's output, i.e., how
frequently is a face detected for different Fitzpatrick skin types or perceived
genders. However, we focus on the robustness of these system outputs under
noisy natural perturbations. We present the first of its kind detailed
benchmark of the robustness of three such systems: Amazon Rekognition,
Microsoft Azure, and Google Cloud Platform. We use both standard and recently
released academic facial datasets to quantitatively analyze trends in
robustness for each. Across all the datasets and systems, we generally find
that photos of individuals who are older, masculine presenting, of darker skin
type, or have dim lighting are more susceptible to errors than their
counterparts in other identities.

本文关注人脸检测系统的输出鲁棒性，使用 Amazon Rekognition、Microsoft Azure 和 Google Cloud Platform 三种系统，通过标准和最近发布的学术数据集量化分析它们的鲁棒性，发现较老、男性化、深肤色或光线较暗的人的照片更容易产生错误。

商用人脸检测中的鲁棒性差异

Robustness Disparities in Commercial Face Detection

In this paper, we describe our algorithmic approach, which was used for
submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017)
group-level emotion recognition sub-challenge. We extracted feature vectors of
detected faces using the Convolutional Neural Network trained for face
identification task, rather than traditional pre-training on emotion
recognition problems. In the final pipeline an ensemble of Random Forest
classifiers was learned to predict emotion score using available training set.
In case when the faces have not been detected, one member of our ensemble
extracts features from the whole image. During our experimental study, the
proposed approach showed the lowest error rate when compared to other explored
techniques. In particular, we achieved 75.4% accuracy on the validation data,
which is 20% higher than the handcrafted feature-based baseline. The source
code using Keras framework is publicly available.

本文描述了我们在第五届 Emotion Recognition in the Wild（EmotiW 2017）小组级情感识别子挑战赛中使用的算法方法，其中我们使用了卷积神经网络来提取检测到的脸部的特征向量，而不是传统的预训练情感识别问题。最终，我们的管道中学习了一个随机森林集成分类器来预测情感分数，该集成分类器在实验研究中显示出与其他探索技术相比最低的错误率。