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Face Matching for Post-Disaster Family Reunification

机译：灾后家庭团聚的人脸匹配

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The National Library of Medicine (NLM) has developed People Locator TM(PL), a Web-based system for family reunification in cases of a natural or man-made disaster. PL accepts photos and brief text meta-data (name, age, etc.) of missing or found persons. Searchers may query PL with text information, but text data is often incomplete or inconsistent. Adding an image-based search capability, i.e., matching faces in query photos to those already stored in the system, would significantly benefit the user experience. We report on our face matching R&D that aims to provide robust face localization and matching on digital photos of variable quality. In this article, we review relevant research and present our approach to robust near-duplicate image detection as well as face matching. We describe the integration of our face matching system with PL, report on its performance, and compare it to other publicly available face recognition systems. In contrast to these systems that have many good quality well-illuminated sample images for each person, our algorithms are hampered by the lack of training examples for individual faces, as those are unlikely in a disaster setting.
机译：国家医学图书馆（NLM）开发了People Locator TM（PL），这是一个基于Web的系统，用于在自然或人为灾难中实现家庭团聚。 PL接受失踪或被发现人员的照片和简短的文字元数据（姓名，年龄等）。搜索者可以用文本信息查询PL，但是文本数据通常不完整或不一致。添加基于图像的搜索功能，即将查询照片中的面部与已经存储在系统中的面部进行匹配，将极大地改善用户体验。我们报告了人脸匹配研发，旨在提供可靠的人脸定位和可变质量的数码照片匹配。在本文中，我们回顾了相关研究，并提出了用于稳健的近重复图像检测以及面部匹配的方法。我们描述了人脸匹配系统与PL的集成，报告其性能，并将其与其他公共可用的人脸识别系统进行比较。与这些系统为每个人提供许多质量良好且照明良好的样本图像相反，我们的算法因缺少针对单个面孔的训练示例而受到阻碍，因为在灾难情况下不太可能使用这些示例。

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