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Hierarchical On-line Appearance-Based Tracking for 3D head pose, eyebrows, lips,eyelids and irises

机译:基于分层在线外观的3D头部姿势,眉毛,嘴唇,眼睑和虹膜跟踪

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摘要

In this paper, we propose an On-line Appearance-Based Tracker (OABT) for simultaneous tracking of 3D head pose, lips, eyebrows, eyelids and irises in monocular video sequences. In contrast to previously proposed tracking approaches, which deal with face and gaze tracking separately, our OABT can also be used for eyelid and iris tracking, as well as 3D head pose, lips and eyebrows facial actions tracking. Furthermore, our approach applies an on-line learning of changes in the appearance of the tracked target. Hence, the prior training of appearance models, which usually requires a large amount of labeled facial images, is avoided. Moreover, the proposed method is built upon a hierarchical combination of three OABTs, which are optimized using a Levenberg-Marquardt Algorithm (LMA) enhanced with line-search procedures. This, in turn, makes the proposed method robust to changes in lighting conditions, occlusions and translucent textures, as evidenced by our experiments. Finally, the proposed method achieves head and facial actions tracking in real-time.
机译:在本文中,我们提出了一种基于在线外观的跟踪器(OABT),用于同时跟踪单眼视频序列中的3D头部姿势,嘴唇,眉毛,眼睑和虹膜。与先前提出的分别处理面部和凝视跟踪的跟踪方法相比,我们的OABT还可以用于眼睑和虹膜跟踪以及3D头部姿势,嘴唇和眉毛面部动作跟踪。此外,我们的方法对跟踪目标的外观变化进行在线学习。因此,避免了通常需要大量标记面部图像的外观模型的先前训练。此外,所提出的方法建立在三个OABT的分层组合的基础上,使用通过行搜索程序增强的Levenberg-Marquardt算法(LMA)对其进行了优化。反过来,这使所提出的方法对光照条件,遮挡和半透明纹理的变化具有鲁棒性,正如我们的实验所证明的那样。最后,该方法实现了头部和面部动作的实时跟踪。

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