Face Association across Unconstrained Video Frames Using Conditional Random Fields
Face Association across Unconstrained Video Frames Using Conditional Random Fields
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DOI：
10.1007/978-3-642-33786-4_13
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
Ming-hui Du;R. Chellappa
中科院分区：
文献类型：
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作者：
Ming-hui Du;R. Chellappa
跨无约束视频帧的自动人脸关联具有许多实际应用。目标检测领域的最新进展使得用更鲁棒的基于检测的方法取代传统的基于跟踪的关联方法成为可能。然而，对于真实世界的无约束视频来说，这仍然是一个非常具有挑战性的任务，特别是如果主体处于移动平台中并且距离超过几十米。在本文中，我们提出了一种新的解决方案的基础上的条件随机场（CRF）的框架。CRF方法不仅给出了一个概率和系统的处理问题，但也优雅地结合了全球和本地的功能。当标签中的模糊性不能通过单独使用面部外观来解决时，我们的方法依赖于多个上下文特征来提供进一步的关联证据。我们的算法在在线模式下工作，能够可靠地处理真实世界的视频。使用具有挑战性的视频数据的实验结果和与其他方法的比较证明了我们的方法的有效性。
Automatic face association across unconstrained video frames has many practical applications. Recent advances in the area of object detection have made it possible to replace the traditional tracking-based association approaches with the more robust detection-based ones. However, it is still a very challenging task for real-world unconstrained videos, especially if the subjects are in a moving platform and at distances exceeding several tens of meters. In this paper, we present a novel solution based on a Conditional Random Field (CRF) framework. The CRF approach not only gives a probabilistic and systematic treatment of the problem, but also elegantly combines global and local features. When ambiguities in labels cannot be solved by using the face appearance alone, our method relies on multiple contextual features to provide further evidence for association. Our algorithm works in an on-line mode and is able to reliably handle real-world videos. Results of experiments using challenging video data and comparisons with other methods are provided to demonstrate the effectiveness of our method.