The accuracy of face recognition systems has improved significantly in the past few years, thanks to the large amount of data collected and the advancement in neural network architectures. However, these large-scale datasets are often collected without explicit consent, raising ethical and privacy concerns. To address this, there have been proposals to use synthetic datasets for training face recognition models. Yet, such models still rely on real data to train the generative models and generally exhibit inferior performance compared to those trained on real datasets. One of these datasets, DigiFace, uses a graphics pipeline to generate different identities and different intra-class variations without using real data in training the models. However, the performance of this approach is poor on face recognition benchmarks, possibly due to the lack of realism in the images generated from the graphics pipeline. In this work, we introduce a novel framework for realism transfer aimed at enhancing the realism of synthetically generated face images. Our method leverages the large-scale face foundation model, and we adapt the pipeline for realism enhancement. By integrating the controllable aspects of the graphics pipeline with our realism enhancement technique, we generate a large amount of realistic variations-combining the advantages of both approaches. Our empirical evaluations demonstrate that models trained using our enhanced dataset significantly improve the performance of face recognition systems over the baseline. The source code and datasets will be made available publicly.

翻译：过去几年，得益于大量数据收集和神经网络架构的进步，人脸识别系统的准确性显著提高。然而，这些大规模数据集通常未经明确同意收集，引发了伦理和隐私问题。为解决此问题，已有研究提出使用合成数据集训练人脸识别模型。然而，此类模型仍依赖真实数据训练生成模型，且通常表现逊色于在真实数据集上训练的模型。其中，DigiFace数据集利用图形管线生成不同身份和类内变化，而无需在模型训练中使用真实数据。但该方法在人脸识别基准测试中表现不佳，可能源于图形管线生成图像缺乏真实感。本文提出一种新颖的真实感迁移框架，旨在增强合成生成人脸图像的真实感。我们的方法利用大规模人脸基础模型，并调整了真实感增强管线。通过将图形管线的可控方面与我们的真实感增强技术相结合，我们生成了大量真实感变化——融合了两种方法的优势。实证评估表明，使用我们增强数据集训练的模型显著提升了人脸识别系统性能，优于基线方法。源代码和数据集将公开提供。