发表机构
Idiap Research Institute（伊迪阿普研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
介绍用于远距离人员识别的多模态数据集GaitFace，利用预注册和“野外”捕获数据反映真实边境场景，通过基准测试揭示当前面部和步态模型在低分辨率和高视角下的关键漏洞，为无约束生物识别研究提供公共基准。
AI 中文摘要
高效的边境管控正成为一项重大的全球挑战，主要是由于严重拥堵和旅客等待时间延长。为缓解这些瓶颈并促进客流，生物识别技术越来越多地被用于简化身份验证并提高通关效率。技术限制常常阻碍生物识别，尤其是在远距离监控中，系统必须应对恶劣的大气条件和图像质量下降的问题。虽然像BRIAR这样的高质量框架存在，但它们通常仅限于特定政府机构。本文介绍了GaitFace，一个包含远距离捕获的面部和步态数据的新公共数据集。为确保研究反映真实的边境场景，我们使用预注册数据（旅客通过移动设备注册）和“野外”捕获数据（从多个视角和不同摄像头远距离记录个人）。对SOTA面部和步态模型进行基准测试表明，尽管有光学辅助，当前架构在低分辨率和高视角下仍会失败。GaitFace揭示了这些关键漏洞，为推动更强大、无约束的生物识别研究提供了严格的公共基准。
英文摘要
Efficient border control is becoming a significant global challenge, mainly due to severe congestion and extended passenger waiting times. To mitigate these bottlenecks and facilitate passenger flow, biometric technologies are increasingly deployed to streamline identity verification and enhance crossing efficiency. Technical limitations frequently impede biometric identification, particularly in long-range surveillance, where systems must deal with adverse atmospheric conditions and degraded image quality. While high-quality frameworks like BRIAR exist, they are frequently restricted to specific government agencies. This paper introduces GaitFace, a new public dataset that contains face and gait data captured at long distances. To ensure that the research reflects authentic border scenarios, we use Pre-Enrollment data, where a traveler registers via a mobile device, and "In-the-Wild" captures, which records individuals at a distance across multiple viewing angles and different cameras. Benchmarking SOTA face and gait models reveals that current architectures fail under low-resolution and elevated viewpoints despite success with optical assistance. GaitFace exposes these critical vulnerabilities, providing a rigorous public benchmark to drive more robust, unconstrained biometric research.
CommentsAccepted in IJCB 2026 , see https://idiap.ch/paper/gaitface/
