StyleGAN 潜在空间中保留身份的人脸老化与去老化
Identity-Preserving Aging and De-Aging of Faces in the StyleGAN Latent Space
AI总结：
本研究针对现有生成式人脸老化/去老化方法条件控制复杂、身份保留缺乏保障的问题，提出通过编辑StyleGAN2潜在空间、结合支持向量建模与特征选择的方法，在保留身份的同时实现人脸年龄变换，并生成公开合成人脸数据集支撑相关基准测试。
摘要：
生成式AI驱动的人脸老化与去老化技术因在法医学、安防及媒体等领域的应用而备受关注。然而，当前大多数先进方法依赖条件生成对抗网络（GANs）、基于扩散的模型或视觉语言模型（VLMs），通过预定义年龄类别，并借助损失函数、微调或文本提示进行条件控制来实现人脸老化或去老化。这种对条件控制的依赖导致训练要求复杂、数据需求增加，且难以生成一致的结果。此外，身份保留问题很少被纳入考量，或仅在单一人脸识别系统上进行评估，无法对生成的老化/去老化人脸是否能保留身份提供任何控制或保证。本文提出通过对老化/去老化方向进行简单支持向量建模，并结合多种特征选择方法，编辑StyleGAN2的潜在空间来合成老化与去老化的人脸。我们使用两套先进的人脸识别系统，通过实验在StyleGAN2潜在空间中找到了保留身份的子空间，从而能够在改变给定人脸表观年龄的同时保留其身份。随后，我们提出了一个简单且实用的公式，用于估算老化/去老化参数的限值，确保给定输入人脸的身份得以保留。利用我们的方法和估算出的参数，我们生成了一个包含不同年龄合成人脸的公开数据集，可用于基准测试跨年龄人脸识别、年龄验证系统或合成图像检测系统。我们的代码和数据集可在项目页面https://www.idiap.ch/paper/agesynth/ 获取。
英文摘要：
Face aging or de-aging with generative AI has gained significant attention for its applications in such fields like forensics, security, and media. However, most state of the art methods rely on conditional Generative Adversarial Networks (GANs), Diffusion-based models, or Visual Language Models (VLMs) to age or de-age faces based on predefined age categories and conditioning via loss functions, fine-tuning, or text prompts. The reliance on such conditioning leads to complex training requirements, increased data needs, and challenges in generating consistent results. Additionally, identity preservation is rarely taken into accountor evaluated on a single face recognition system without any control or guarantees on whether identity would be preserved in a generated aged/de-aged face. In this paper, we propose to synthesize aged and de-aged faces via editing latent space of StyleGAN2 using a simple support vector modeling of aging/de-aging direction and several feature selection approaches. By using two state-of-the-art face recognition systems, we empirically find the identity preserving subspace within the StyleGAN2 latent space, so that an apparent age of a given face can changed while preserving the identity. We then propose a simple yet practical formula for estimating the limits on aging/de-aging parameters that ensures identity preservation for a given input face. Using our method and estimated parameters we have generated a public dataset of synthetic faces at different ages that can be used for benchmarking cross-age face recognition, age assurance systems, or systems for detection of synthetic images. Our code and dataset are available at the project page https://www.idiap.ch/paper/agesynth/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
