Many of the commonly used datasets for face recognition development are collected from the internet without proper user consent. Due to the increasing focus on privacy in the social and legal frameworks, the use and distribution of these datasets are being restricted and strongly questioned. These databases, which have a realistically high variability of data per identity, have enabled the success of face recognition models. To build on this success and to align with privacy concerns, synthetic databases, consisting purely of synthetic persons, are increasingly being created and used in the development of face recognition solutions. In this work, we present a three-player generative adversarial network (GAN) framework, namely IDnet, that enables the integration of identity information into the generation process. The third player in our IDnet aims at forcing the generator to learn to generate identity-separable face images. We empirically proved that our IDnet synthetic images are of higher identity discrimination in comparison to the conventional two-player GAN, while maintaining a realistic intra-identity variation. We further studied the identity link between the authentic identities used to train the generator and the generated synthetic identities, showing very low similarities between these identities. We demonstrated the applicability of our IDnet data in training face recognition models by evaluating these models on a wide set of face recognition benchmarks. In comparison to the state-of-the-art works in synthetic-based face recognition, our solution achieved comparable results to a recent rendering-based approach and outperformed all existing GAN-based approaches. The training code and the synthetic face image dataset are publicly available ( https://github.com/fdbtrs/Synthetic-Face-Recognition ).

翻译：当前人脸识别开发中常用的大多数数据集均从互联网收集，未获得用户适当同意。随着社会与法律框架对隐私问题的日益关注，这些数据集的使用与分发正面临限制与强烈质疑。这些数据库因每个身份具有较高的真实数据变异性，从而推动了人脸识别模型的成功。为延续这一成功并兼顾隐私关切，仅由合成人物构成的合成数据库正越来越多地被创建并应用于人脸识别解决方案的开发中。本文提出了一种三玩家生成对抗网络（GAN）框架——IDnet，该框架能够将身份信息集成到生成过程中。我们IDnet中的第三玩家旨在迫使生成器学习生成身份可分离的人脸图像。实验证明，与传统的双玩家GAN相比，我们的IDnet合成图像具有更高的身份辨别能力，同时保持了逼真的身份内变异。我们进一步研究了用于训练生成器的真实身份与生成的合成身份之间的身份关联，发现这些身份之间的相似性极低。通过在一系列广泛的人脸识别基准上评估模型，我们展示了IDnet数据在训练人脸识别模型中的适用性。与当前基于合成人脸识别的先进方法相比，我们的解决方案取得了与近期基于渲染的方法相当的结果，并优于所有现有的基于GAN的方法。训练代码与合成人脸图像数据集已公开提供（https://github.com/fdbtrs/Synthetic-Face-Recognition）。