Abstract
Traditional face recognition systems rely on extracting fixed face representations, known as templates, to store and verify identities. These representations are typically generated by neural networks that often lack explainability and raise concerns regarding fairness and privacy. In this work, we propose a novel model-template (MOTE) approach that replaces vector-based face templates with small personalized neural networks. This design enables more responsible face recognition for small and medium-scale systems. During enrollment, MOTE creates a dedicated binary classifier for each identity, trained to determine whether an input face matches the enrolled identity. Each classifier is trained using only a single reference sample, along with synthetically balanced samples to allow adjusting fairness at the level of a single individual during enrollment. Extensive experiments across multiple datasets and recognition systems demonstrate substantial improvements in fairness and particularly in privacy. Although the method increases inference time and storage requirements, it presents a strong solution for small- and mid-scale applications where fairness and privacy are critical.
Abstract (translated)
传统的面部识别系统依赖于提取固定的面部表示，即模板，来存储和验证身份。这些表示通常由神经网络生成，但往往缺乏可解释性，并且在公平性和隐私方面存在担忧。在这项工作中，我们提出了一种新颖的模型-模板（MOTE）方法，该方法用小型个性化神经网络替代基于向量的面部模板。这种设计使小规模和中等规模系统中的面部识别更加负责任。 在注册阶段，MOTE为每个身份创建一个专用的二元分类器，并训练它来确定输入的面部是否与已注册的身份匹配。每个分类器仅使用单个参考样本进行训练，并结合合成平衡样本以允许在个人层面上调整公平性。广泛的跨多个数据集和识别系统的实验表明，在公平性和隐私方面有了显著改进。 尽管该方法增加了推理时间和存储需求，但对于小型和中型应用（这些应用对公平性和隐私有严格要求）来说，它提供了一个强有力的解决方案。
URL
https://arxiv.org/abs/2505.19920
