Abstract
Advancements in face recognition (FR) technologies have amplified privacy concerns, necessitating methods that protect identity while maintaining recognition utility. Existing face anonymization methods typically focus on obscuring identity but fail to meet the requirements of biometric template protection, including revocability, unlinkability, and irreversibility. We propose FaceAnonyMixer, a cancelable face generation framework that leverages the latent space of a pre-trained generative model to synthesize privacy-preserving face images. The core idea of FaceAnonyMixer is to irreversibly mix the latent code of a real face image with a synthetic code derived from a revocable key. The mixed latent code is further refined through a carefully designed multi-objective loss to satisfy all cancelable biometric requirements. FaceAnonyMixer is capable of generating high-quality cancelable faces that can be directly matched using existing FR systems without requiring any modifications. Extensive experiments on benchmark datasets demonstrate that FaceAnonyMixer delivers superior recognition accuracy while providing significantly stronger privacy protection, achieving over an 11% gain on commercial API compared to recent cancelable biometric methods. Code is available at: this https URL.
Abstract (translated)
面部识别技术的进步引发了隐私问题，因此需要既能保护个人身份又能保持识别功能的方法。现有的面部匿名化方法通常专注于模糊身份信息，但未能满足生物特征模板保护的要求，包括可撤销性、不可链接性和不可逆性。我们提出了一种名为FaceAnonyMixer的框架，它利用预训练生成模型的潜在空间来合成具有隐私保护特性的面部图像。 FaceAnonyMixer的核心思想是将真实面部图像的潜在代码与从可撤销密钥中衍生出的合成代码进行不可逆混合。然后通过精心设计的多目标损失函数进一步优化混合后的潜在代码，以满足所有生物特征取消的要求。FaceAnonyMixer能够生成高质量且可以直接使用现有面部识别系统进行匹配的可撤销面部图像，并不需要对这些系统做任何修改。 在基准数据集上的广泛实验表明，FaceAnonyMixer能够在提供显著更强隐私保护的同时实现更优的人脸识别准确率，在商用API上相比最近的生物特征取消方法提升了超过11%。代码可以在以下链接获取：[此URL]（请将“this https URL”替换为实际提供的GitHub或其他版本控制平台上的链接）。
URL
https://arxiv.org/abs/2508.05636
