用个性化神经网络替代传统人脸模板，提升小中规模系统的公平与隐私
A Responsible Face Recognition Approach for Small and Mid-Scale Systems Through Personalized Neural Networks
- 为每个身份创建专用二分类神经网络，仅需单张样本训练
- 在单人层面实现公平性调整，多数据集验证隐私与公平性显著提升
- 适合对公平性和隐私要求高的中小型人脸识别场景
传统人脸系统依赖固定向量模板存储和验证身份，这些模板由缺乏可解释性的神经网络生成，引发公平与隐私担忧。本文提出新型模型-模板（MOTE）方法，以小型个性化神经网络替代向量模板。注册阶段，MOTE为每位身份创建专用二分类器，仅用一张参考样本及合成平衡样本训练，实现个体层面的公平性调节。多个数据集和识别系统上的大量实验表明，该方法在公平性和隐私方面均有显著提升。尽管推理时间和存储需求增加，但为对公平与隐私敏感的小中型应用提供了强有力解决方案。
原文摘要 · Abstract (English)
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.
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