Achieving state-of-the-art results in face verification systems typically
hinges on the availability of labeled face training data, a resource that often
proves challenging to acquire in substantial quantities. In this research
endeavor, we proposed employing Siamese networks for face recognition,
eliminating the need for labeled face images. We achieve this by strategically
leveraging negative samples alongside nearest neighbor counterparts, thereby
establishing positive and negative pairs through an unsupervised methodology.
The architectural framework adopts a VGG encoder, trained as a double branch
siamese network. Our primary aim is to circumvent the necessity for labeled
face image data, thus proposing the generation of training pairs in an entirely
unsupervised manner. Positive training data are selected within a dataset based
on their highest cosine similarity scores with a designated anchor, while
negative training data are culled in a parallel fashion, though drawn from an
alternate dataset. During training, the proposed siamese network conducts
binary classification via cross-entropy loss. Subsequently, during the testing
phase, we directly extract face verification scores from the network's output
layer. Experimental results reveal that the proposed unsupervised system
delivers a performance on par with a similar but fully supervised baseline.

通过使用对比网络的方法，在没有标记的人脸图像的情况下，实现了接近监督基准的性能。

基于 Siamese 网络的深度学习人脸识别方法

Deep Learning Based Face Recognition Method using Siamese Network

Deep Learning systems need large data for training. Datasets for training
face verification systems are difficult to obtain and prone to privacy issues.
Synthetic data generated by generative models such as GANs can be a good
alternative. However, we show that data generated from GANs are prone to bias
and fairness issues. Specifically, GANs trained on FFHQ dataset show biased
behavior towards generating white faces in the age group of 20-29. We also
demonstrate that synthetic faces cause disparate impact, specifically for race
attribute, when used for fine tuning face verification systems.

本文介绍了深度学习系统需要大量数据进行训练的主题，提出了使用生成模型（如 GAN）生成合成数据的替代方法。然而，我们证明使用 GAN 生成的数据容易存在偏见和公平性问题，尤其是针对年龄段在 20-29 岁之间的白人脸部图像生成存在偏见。此外，我们还证明了在微调人脸验证系统时，合成人脸会对种族属性造成不公平影响。