AI systems rely on extensive training on large datasets to address various
tasks. However, image-based systems, particularly those used for demographic
attribute prediction, face significant challenges. Many current face image
datasets primarily focus on demographic factors such as age, gender, and skin
tone, overlooking other crucial facial attributes like hairstyle and
accessories. This narrow focus limits the diversity of the data and
consequently the robustness of AI systems trained on them. This work aims to
address this limitation by proposing a methodology for generating synthetic
face image datasets that capture a broader spectrum of facial diversity.
Specifically, our approach integrates a systematic prompt formulation strategy,
encompassing not only demographics and biometrics but also non-permanent traits
like make-up, hairstyle, and accessories. These prompts guide a
state-of-the-art text-to-image model in generating a comprehensive dataset of
high-quality realistic images and can be used as an evaluation set in face
analysis systems. Compared to existing datasets, our proposed dataset proves
equally or more challenging in image classification tasks while being much
smaller in size.

通过提出一种生成综合面部多样性数据集的方法，本研究旨在解决现有面部图像数据集集中在年龄、性别和肤色等少数面部属性上的问题，从而提高 AI 系统的鲁棒性。这种方法不仅包括人口统计学和生物识别特征，还涵盖了妆容、发型和配饰等非永久性特征，通过引导最先进的文本到图像模型生成高质量、逼真的综合数据集，并可用于面部分析系统的评估。与现有数据集相比，我们提出的数据集在图像分类任务中同样具有或更高的挑战性，同时大小更小。

SDFD: 构建具有多样属性的通用合成人脸图像数据集

SDFD: Building a Versatile Synthetic Face Image Dataset with Diverse  Attributes

Text-to-image generative AI models such as Stable Diffusion are used daily by
millions worldwide. However, many have raised concerns regarding how these
models amplify racial and gender stereotypes. To study this phenomenon, we
develop a classifier to predict the race, gender, and age group of any given
face image, and show that it achieves state-of-the-art performance. Using this
classifier, we quantify biases in Stable Diffusion across six races, two
genders, five age groups, 32 professions, and eight attributes. We then propose
novel debiasing solutions that outperform state-of-the-art alternatives.
Additionally, we examine the degree to which Stable Diffusion depicts
individuals of the same race as being similar to one another. This analysis
reveals a high degree of stereotyping, e.g., depicting most middle eastern
males as being dark-skinned, bearded, and wearing a traditional headdress. We
address these limitations by proposing yet another novel solution that
increases facial diversity across genders and racial groups. Our solutions are
open-sourced and made publicly available.

使用分类器研究文中文字图生成 AI 模型中的种族和性别刻板印象，提出一种新的去偏方法并增加人脸多样性。

摆脱种族和性别刻板印象的人工智能生成的人脸

AI-generated faces free from racial and gender stereotypes

Existing text-to-image synthesis methods generally are only applicable to
words in the training dataset. However, human faces are so variable to be
described with limited words. So this paper proposes the first free-style
text-to-face method namely AnyFace enabling much wider open world applications
such as metaverse, social media, cosmetics, forensics, etc. AnyFace has a novel
two-stream framework for face image synthesis and manipulation given arbitrary
descriptions of the human face. Specifically, one stream performs text-to-face
generation and the other conducts face image reconstruction. Facial text and
image features are extracted using the CLIP (Contrastive Language-Image
Pre-training) encoders. And a collaborative Cross Modal Distillation (CMD)
module is designed to align the linguistic and visual features across these two
streams. Furthermore, a Diverse Triplet Loss (DT loss) is developed to model
fine-grained features and improve facial diversity. Extensive experiments on
Multi-modal CelebA-HQ and CelebAText-HQ demonstrate significant advantages of
AnyFace over state-of-the-art methods. AnyFace can achieve high-quality,
high-resolution, and high-diversity face synthesis and manipulation results
without any constraints on the number and content of input captions.

本文提出了一种名为 AnyFace 的新型自由文本生成人脸图像的方法，它具有一种新颖的两流框架来进行人脸图像的合成和操作，利用 CLIP 编码器提取面部文本和图像特征并设计了可协同跨模态蒸馏模块，还开发了一种多样化三元组损失来改善人脸多样性。实验证明，AnyFace 可以实现高质量、高分辨率、高多样性的人脸合成和操作结果，而不受输入标题数量和内容的限制。

AnyFace：自由风格的文本到人脸合成与操作

AnyFace: Free-style Text-to-Face Synthesis and Manipulation

Face recognition is a long standing challenge in the field of Artificial
Intelligence (AI). The goal is to create systems that accurately detect,
recognize, verify, and understand human faces. There are significant technical
hurdles in making these systems accurate, particularly in unconstrained
settings due to confounding factors related to pose, resolution, illumination,
occlusion, and viewpoint. However, with recent advances in neural networks,
face recognition has achieved unprecedented accuracy, largely built on
data-driven deep learning methods. While this is encouraging, a critical aspect
that is limiting facial recognition accuracy and fairness is inherent facial
diversity. Every face is different. Every face reflects something unique about
us. Aspects of our heritage - including race, ethnicity, culture, geography -
and our individual identify - age, gender, and other visible manifestations of
self-expression, are reflected in our faces. We expect face recognition to work
equally accurately for every face. Face recognition needs to be fair. As we
rely on data-driven methods to create face recognition technology, we need to
ensure necessary balance and coverage in training data. However, there are
still scientific questions about how to represent and extract pertinent facial
features and quantitatively measure facial diversity. Towards this goal,
Diversity in Faces (DiF) provides a data set of one million annotated human
face images for advancing the study of facial diversity. The annotations are
generated using ten well-established facial coding schemes from the scientific
literature. The facial coding schemes provide human-interpretable quantitative
measures of facial features. We believe that by making the extracted coding
schemes available on a large set of faces, we can accelerate research and
development towards creating more fair and accurate facial recognition systems.

利用深度学习技术和一个由 100 万个带有十个基础面部编码方案的人脸图像组成的数据集，促进对于面部多样性的研究和发展，以提高面部识别系统的公平性和准确性。