Offline Deformable Face Tracking in Arbitrary Videos
Offline Deformable Face Tracking in Arbitrary Videos
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DOI：
10.1109/iccvw.2015.126
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发表时间：
2015-12
期刊：
影响因子：
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通讯作者：
Grigorios G. Chrysos;Epameinondas Antonakos;S. Zafeiriou;Patrick Snape
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文献类型：
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作者：
Grigorios G. Chrysos;Epameinondas Antonakos;S. Zafeiriou;Patrick Snape
静态图像中的通用人脸检测和人脸标志点定位是机器学习和计算机视觉中最成熟和研究最充分的问题之一。目前，性能最好的人脸检测器实现了大约75-80%的真阳性率，同时保持了较低的假阳性率。此外，表现最好的面部标志定位算法获得低点对点错误超过70%的常见基准图像在无约束条件下捕获。然而，视频中的面部标志跟踪任务却吸引了较少的关注。通常，应用检测跟踪框架，其中在每帧中采用面部检测和地标定位以避免漂移。因此，该解决方案相当于静态图像中的地标检测。从经验上讲，这样一个框架的直接应用程序不能实现更高的性能，平均而言，比静态图像的报告。在本文中，据我们所知，我们首次表明，通用人脸检测和地标定位的结果可用于递归训练功能强大且准确的特定于人的人脸检测器和地标定位方法，用于离线可变形跟踪。所提出的管道可以跟踪在任意条件下捕获的非常具有挑战性的长期序列中的地标。该管道被用作半自动工具来注释300-VW挑战赛的大部分视频。
Generic face detection and facial landmark localization in static imagery are among the most mature and well-studied problems in machine learning and computer vision. Currently, the top performing face detectors achieve a true positive rate of around 75-80% whilst maintaining low false positive rates. Furthermore, the top performing facial landmark localization algorithms obtain low point-to-point errors for more than 70% of commonly benchmarked images captured under unconstrained conditions. The task of facial landmark tracking in videos, however, has attracted much less attention. Generally, a tracking-by-detection framework is applied, where face detection and landmark localization are employed in every frame in order to avoid drifting. Thus, this solution is equivalent to landmark detection in static imagery. Empirically, a straightforward application of such a framework cannot achieve higher performance, on average, than the one reported for static imagery. In this paper, we show for the first time, to the best of our knowledge, that the results of generic face detection and landmark localization can be used to recursively train powerful and accurate person-specific face detectors and landmark localization methods for offline deformable tracking. The proposed pipeline can track landmarks in very challenging long-term sequences captured under arbitrary conditions. The pipeline was used as a semi-automatic tool to annotate the majority of the videos of the 300-VW Challenge.