We propose a novel method that leverages 3D information to automatically
anonymize multi-view RGB-D video recordings of operating rooms (OR). Our
anonymization method preserves the original data distribution by replacing the
faces in each image with different faces so that the data remains suitable for
further downstream tasks. In contrast to established anonymization methods, our
approach localizes faces in 3D space first rather than in 2D space. Each face
is then anonymized by reprojecting a different face back into each camera view,
ultimately replacing the original faces in the resulting images. Furthermore,
we introduce a multi-view RGB-D dataset, captured during a real operation of
experienced surgeons performing laparoscopic surgery on an animal object
(swine), which encapsulates typical characteristics of ORs. Finally, we present
experimental results evaluated on that dataset, showing that leveraging 3D data
can achieve better face localization in OR images and generate more realistic
faces than the current state-of-the-art. There has been, to our knowledge, no
prior work that addresses the anonymization of multi-view OR recordings, nor 2D
face localization that leverages 3D information.

提出一种利用三维信息对手术室的多视角 RGB-D 视频记录进行自动化匿名化的新方法，该匿名化方法通过将每个图像中的人脸替换为不同的人脸来保持原始数据分布的一致性，以便进一步的下游任务。

3D 整体匿名化

3D Holistic OR Anonymization

Predicting attributes from face images in the wild is a challenging computer
vision problem. To automatically describe face attributes from face containing
images, traditionally one needs to cascade three technical blocks --- face
localization, facial descriptor construction, and attribute classification ---
in a pipeline. As a typical classification problem, face attribute prediction
has been addressed using deep learning. Current state-of-the-art performance
was achieved by using two cascaded Convolutional Neural Networks (CNNs), which
were specifically trained to learn face localization and attribute description.
In this paper, we experiment with an alternative way of employing the power of
deep representations from CNNs. Combining with conventional face localization
techniques, we use off-the-shelf architectures trained for face recognition to
build facial descriptors. Recognizing that the describable face attributes are
diverse, our face descriptors are constructed from different levels of the CNNs
for different attributes to best facilitate face attribute prediction.
Experiments on two large datasets, LFWA and CelebA, show that our approach is
entirely comparable to the state-of-the-art. Our findings not only demonstrate
an efficient face attribute prediction approach, but also raise an important
question: how to leverage the power of off-the-shelf CNN representations for
novel tasks.

本论文尝试通过利用深度学习的卷积神经网络 (CNN) 的特征表示技术，并结合传统的人脸定位技术来构建人脸描述子，并使用它进行人脸属性预测，实验结果表明，该方法与目前最先进的方法完全可以匹敌，同时也提出了一个重要的问题：如何利用 CNN 特征表示技术解决一些新颖的问题。