Synthetic data is gaining increasing relevance for training machine learning
models. This is mainly motivated due to several factors such as the lack of
real data and intra-class variability, time and errors produced in manual
labeling, and in some cases privacy concerns, among others. This paper presents
an overview of the 2nd edition of the Face Recognition Challenge in the Era of
Synthetic Data (FRCSyn) organized at CVPR 2024. FRCSyn aims to investigate the
use of synthetic data in face recognition to address current technological
limitations, including data privacy concerns, demographic biases,
generalization to novel scenarios, and performance constraints in challenging
situations such as aging, pose variations, and occlusions. Unlike the 1st
edition, in which synthetic data from DCFace and GANDiffFace methods was only
allowed to train face recognition systems, in this 2nd edition we propose new
sub-tasks that allow participants to explore novel face generative methods. The
outcomes of the 2nd FRCSyn Challenge, along with the proposed experimental
protocol and benchmarking contribute significantly to the application of
synthetic data to face recognition.

这篇论文综述了第二届面向合成数据时代的人脸识别挑战，并提出了应用合成数据进行人脸识别的相关问题和目标。

CVPR 2024 第二届 FRCSyn 挑战：合成数据时代的人脸识别挑战

Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge  in the Era of Synthetic Data

Support Vector Machine (SVM) is a powerful tool in binary classification,
known to attain excellent misclassification rates. On the other hand, many
realworld classification problems, such as those found in medical diagnosis,
churn or fraud prediction, involve misclassification costs which may be
different in the different classes. However, it may be hard for the user to
provide precise values for such misclassification costs, whereas it may be much
easier to identify acceptable misclassification rates values. In this paper we
propose a novel SVM model in which misclassification costs are considered by
incorporating performance constraints in the problem formulation. Specifically,
our aim is to seek the hyperplane with maximal margin yielding
misclassification rates below given threshold values. Such maximal margin
hyperplane is obtained by solving a quadratic convex problem with linear
constraints and integer variables. The reported numerical experience shows that
our model gives the user control on the misclassification rates in one class
(possibly at the expense of an increase in misclassification rates for the
other class) and is feasible in terms of running times.

我们提出了一种新颖的支持向量机模型，通过在问题表达中引入性能约束来考虑误分类成本。具体而言，我们的目标是寻求具有最大间隔的超平面，使得误分类率低于给定的阈值。通过解决一个具有线性约束和整数变量的二次凸问题来获得最大间隔超平面。我们的实验结果表明，我们的模型可以使用户在一个类别上对误分类率进行控制，并且运行时间可行。