Abstract
In tracking face and facial actions of unknown people, it is essential to take into account two components of facial shape variations: shape variation between people and variation caused by different facial actions such as facial expressions. This paper presents a monocular method of tracking faces and facial actions using a multilinear face model that treats interpersonal and intrapersonal shape variations separately. We created this method using a multilinear face model by integrating two different frameworks: particle filter-based tracking for time-dependent facial action and pose estimation and incremental bundle adjustment for person-dependent shape estimation. This unique combination together with multilinear face models is the key to tracking faces and facial actions of arbitrary people in real time with no pre-learned individual face models. Experiments using real video sequences demonstrate the effectiveness of our method.
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Sugano, Y., Sato, Y. (2007). Person-Independent Monocular Tracking of Face and Facial Actions with Multilinear Models. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds) Analysis and Modeling of Faces and Gestures. AMFG 2007. Lecture Notes in Computer Science, vol 4778. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75690-3_5
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DOI: https://doi.org/10.1007/978-3-540-75690-3_5
Publisher Name: Springer, Berlin, Heidelberg
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