Collaborative Face Recognition for Improved Face Annotation in Personal Photo Collections Shared on Online Social Networks
Collaborative Face Recognition for Improved Face Annotation in Personal Photo Collections Shared on Online Social Networks
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DOI：
10.1109/tmm.2010.2087320
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发表时间：
2011-02
影响因子：
7.3
通讯作者：
J. Choi;W. D. Neve;K. Plataniotis;Yong Man Ro
中科院分区：
文献类型：
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作者：
J. Choi;W. D. Neve;K. Plataniotis;Yong Man Ro
在在线社交网络（OSN）中使用人脸注释来有效管理个人照片目前具有相当大的实际意义。在本文中，我们提出了一种新的协同人脸识别（FR）框架，提高人脸标注的准确性，有效地利用多个FR引擎在一个OSN。我们的协作FR框架由两个主要部分组成：FR引擎的选择和多个FR结果的合并（或融合）。FR引擎的选择旨在确定适合于识别属于OSN的特定成员的查询面部图像的个性化FR引擎的集合。为了这个目的，我们利用OSN中的社交网络背景和个人照片集中的社交背景。此外，为了利用从所选FR引擎检索的多个FR结果的可用性，我们设计了两种有效的解决方案，用于合并FR结果，采用传统技术结合多个分类器的结果。使用从现有OSN收集的547991张个人照片进行实验。我们的研究结果表明，所提出的合作FR方法是能够显着提高人脸标注的准确性，相比传统的FR方法，只利用一个FR引擎。此外，我们还证明了我们的协作FR框架具有较低的计算成本，并且具有适合在分散式OSN中部署的设计。
Using face annotation for effective management of personal photos in online social networks (OSNs) is currently of considerable practical interest. In this paper, we propose a novel collaborative face recognition (FR) framework, improving the accuracy of face annotation by effectively making use of multiple FR engines available in an OSN. Our collaborative FR framework consists of two major parts: selection of FR engines and merging (or fusion) of multiple FR results. The selection of FR engines aims at determining a set of personalized FR engines that are suitable for recognizing query face images belonging to a particular member of the OSN. For this purpose, we exploit both social network context in an OSN and social context in personal photo collections. In addition, to take advantage of the availability of multiple FR results retrieved from the selected FR engines, we devise two effective solutions for merging FR results, adopting traditional techniques for combining multiple classifier results. Experiments were conducted using 547 991 personal photos collected from an existing OSN. Our results demonstrate that the proposed collaborative FR method is able to significantly improve the accuracy of face annotation, compared to conventional FR approaches that only make use of a single FR engine. Further, we demonstrate that our collaborative FR framework has a low computational cost and comes with a design that is suited for deployment in a decentralized OSN.