Despite the success of deep-learning models in many tasks, there have been
concerns about such models learning shortcuts, and their lack of robustness to
irrelevant confounders. When it comes to models directly trained on human
faces, a sensitive confounder is that of human identities. Many face-related
tasks should ideally be identity-independent, and perform uniformly across
different individuals (i.e. be fair). One way to measure and enforce such
robustness and performance uniformity is through enforcing it during training,
assuming identity-related information is available at scale. However, due to
privacy concerns and also the cost of collecting such information, this is
often not the case, and most face datasets simply contain input images and
their corresponding task-related labels. Thus, improving identity-related
robustness without the need for such annotations is of great importance. Here,
we explore using face-recognition embedding vectors, as proxies for identities,
to enforce such robustness. We propose to use the structure in the
face-recognition embedding space, to implicitly emphasize rare samples within
each class. We do so by weighting samples according to their conditional
inverse density (CID) in the proxy embedding space. Our experiments suggest
that such a simple sample weighting scheme, not only improves the training
robustness, it often improves the overall performance as a result of such
robustness. We also show that employing such constraints during training
results in models that are significantly less sensitive to different levels of
bias in the dataset.

本研究提出使用人脸识别嵌入向量作为代理标识，通过加权样本来强制实现模型的稳健性和性能均匀性，在不需要注释的情况下提高模型的标识相关性的稳健性和性能。

提高人脸模型身份鲁棒性

Improving Identity-Robustness for Face Models

We demonstrate that it is possible to perform face-related computer vision in
the wild using synthetic data alone. The community has long enjoyed the
benefits of synthesizing training data with graphics, but the domain gap
between real and synthetic data has remained a problem, especially for human
faces. Researchers have tried to bridge this gap with data mixing, domain
adaptation, and domain-adversarial training, but we show that it is possible to
synthesize data with minimal domain gap, so that models trained on synthetic
data generalize to real in-the-wild datasets. We describe how to combine a
procedurally-generated parametric 3D face model with a comprehensive library of
hand-crafted assets to render training images with unprecedented realism and
diversity. We train machine learning systems for face-related tasks such as
landmark localization and face parsing, showing that synthetic data can both
match real data in accuracy as well as open up new approaches where manual
labelling would be impossible.

本研究通过合适的合成数据及其训练方式进行模型的学习及数据的一般化，具有现实中追随的功效及实时性。

虚假到成功：利用合成数据在野外进行面部分析

Fake It Till You Make It: Face analysis in the wild using synthetic data  alone

In this paper, we present a novel single shot face-related task analysis
method, called Face-SSD, for detecting faces and for performing various
face-related (classification/regression) tasks including smile recognition,
face attribute prediction and valence-arousal estimation in the wild. Face-SSD
uses a Fully Convolutional Neural Network (FCNN) to detect multiple faces of
different sizes and recognise/regress one or more face-related classes.
Face-SSD has two parallel branches that share the same low-level filters, one
branch dealing with face detection and the other one with face analysis tasks.
The outputs of both branches are spatially aligned heatmaps that are produced
in parallel - therefore Face-SSD does not require that face detection, facial
region extraction, size normalisation, and facial region processing are
performed in subsequent steps. Our contributions are threefold: 1) Face-SSD is
the first network to perform face analysis without relying on pre-processing
such as face detection and registration in advance - Face-SSD is a simple and a
single FCNN architecture simultaneously performing face detection and
face-related task analysis - those are conventionally treated as separate
consecutive tasks; 2) Face-SSD is a generalised architecture that is applicable
for various face analysis tasks without modifying the network structure - this
is in contrast to designing task-specific architectures; and 3) Face-SSD
achieves real-time performance (21 FPS) even when detecting multiple faces and
recognising multiple classes in a given image. Experimental results show that
Face-SSD achieves state-of-the-art performance in various face analysis tasks
by reaching a recognition accuracy of 95.76% for smile detection, 90.29% for
attribute prediction, and Root Mean Square (RMS) error of 0.44 and 0.39 for
valence and arousal estimation.

本文提出了一种名为 Face-SSD 的新颖单次快照人脸相关任务分析方法，使用完全卷积神经网络检测各种规模的多个面孔，并识别 / 回归一个或多个面部相关类别。Face-SSD 是第一个不依赖于预处理（如提前脸部检测和配准）即可执行人脸分析的网络，具有实时性能，同时可以检测多个面孔并识别多个类别，为各种面部分析任务提供了具有广泛适应性的架构，并在各项面部分析任务中实现了最先进的性能。