Face recognition (FR) has reached a high technical maturity. However, its use needs to be carefully assessed from an ethical perspective, especially in sensitive scenarios. This is precisely the focus of this paper: the use of FR for the identification of specific subjects in moderately to densely crowded spaces (e.g. public spaces, sports stadiums, train stations) and law enforcement scenarios. In particular, there is a need to consider the trade-off between the need to protect privacy and fundamental rights of citizens as well as their safety. Recent Artificial Intelligence (AI) policies, notably the European AI Act, propose that such FR interventions should be proportionate and deployed only when strictly necessary. Nevertheless, concrete guidelines on how to address the concept of proportional FR intervention are lacking to date. This paper proposes a framework to contribute to assessing whether an FR intervention is proportionate or not for a given context of use in the above mentioned scenarios. It also identifies the main quantitative and qualitative variables relevant to the FR intervention decision (e.g. number of people in the scene, level of harm that the person(s) in search could perpetrate, consequences to individual rights and freedoms) and propose a 2D graphical model making it possible to balance these variables in terms of ethical cost vs security gain. Finally, different FR scenarios inspired by real-world deployments validate the proposed model. The framework is conceived as a simple support tool for decision makers when confronted with the deployment of an FR system.

翻译：人脸识别技术已具备高度成熟性，但其应用需从伦理视角进行审慎评估，尤其在敏感场景中。这正是本文的核心关注点：探讨在中等至高度密集空间（如公共场所、体育场馆、火车站）及执法场景中，利用人脸识别技术识别特定主体的应用。具体而言，需权衡保护公民隐私权与基本权利同保障公共安全之间的平衡。近期人工智能政策（特别是《欧洲人工智能法案》）提出，此类人脸识别干预措施应遵循适度原则，仅在绝对必要时方可部署。然而，当前尚无具体指南阐释如何实现适度的人脸识别干预。本文提出一个评估框架，旨在判定在上述场景的特定使用情境下，人脸识别干预是否具有适度性。该框架识别了影响干预决策的关键量化与质性变量（例如场景中的人数、目标对象可能造成的危害程度、对个人权利与自由的潜在影响），并提出二维图形模型，以在伦理成本与安全收益之间实现变量平衡。最后，基于真实部署场景的多种人脸识别案例验证了该模型的有效性。该框架旨在为决策者在部署人脸识别系统时提供简易决策支持工具。