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Supervised coordinate descent method with a 3D bilinear model for face alignment and tracking.

Zhang, Y., Liu, S., Yang, X., Zhang, J. J. and Shi, D., 2017. Supervised coordinate descent method with a 3D bilinear model for face alignment and tracking. Computer Animation & Virtual Worlds, 28 (3-4), e1773.

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Tmpl_paj.pdf - Accepted Version


DOI: 10.1002/cav.1773


Face alignment and tracking play important roles in facial performance capture. Existing data-driven methods for monocular videos suffer from large variations of pose and expression. In this paper, we propose an efficient and robust method for this task by introducing a novel supervised coordinate descent method with 3D bilinear representation. Instead of learning the mapping between the whole parameters and image features directly with a cascaded regression framework in current methods, we learn individual sets of parameters mappings separately step by step by a coordinate descent mean. Because different parameters make different contributions to the displacement of facial landmarks, our method is more discriminative to current whole-parameter cascaded regression methods. Benefiting from a 3D bilinear model learned from public databases, the proposed method can handle the head pose changes and extreme expressions out of plane better than other 2D-based methods. We present the reliable result of face tracking under various head poses and facial expressions on challenging video sequences collected online. The experimental results show that our method outperforms state-of-art data-driven methods.

Item Type:Article
Uncontrolled Keywords:face alignment; face tracking; facial performance capture; supervised coordinate descent method
Group:Faculty of Media & Communication
ID Code:29349
Deposited By: Symplectic RT2
Deposited On:16 Jun 2017 11:37
Last Modified:14 Mar 2022 14:05


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