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Multilinear wavelets: A statistical shape space for human faces

: Brunton, Alan; Bolkart, Timo; Wuhrer, Stefanie


Fleet, David (Ed.):
Computer vision - ECCV 2014. Pt.1 : 13th European conference, Zurich, Switzerland, September 6 - 12, 2014; Proceedings
Cham: Springer International Publishing, 2014 (Lecture Notes in Computer Science 8689)
ISBN: 978-3-319-10589-5 (Print)
ISBN: 978-3-319-10590-1 (Online)
European Conference on Computer Vision (ECCV) <13, 2014, Zurich>
Conference Paper
Fraunhofer IGD ()
statistical shape models (SSM); Wavelets; 3d cameras; 3D Data; 3D data acquisition; 3D data processing; 3D data representation; 3D face recognition; 3D Interaction; 3D model acquisition; 3D Model Reconstruction; 3d modeling; 3D tracking; face tracking; facial expression analysis; statistics; surface representation

We present a statistical model for 3D human faces in varying expression, which decomposes the surface of the face using a wavelet transform, and learns many localized, decorrelated multilinear models on the resulting coefficients. Using this model we are able to reconstruct faces from noisy and occluded 3D face scans, and facial motion sequences. Accurate reconstruction of face shape is important for applications such as tele-presence and gaming. The localized and multi-scale nature of our model allows for recovery of fine-scale detail while retaining robustness to severe noise and occlusion, and is computationally efficient and scalable. We validate these properties experimentally on challenging data in the form of static scans and motion sequences. We show that in comparison to a global multilinear model, our model better preserves fine detail and is computationally faster, while in comparison to a localized PCA model, our model better handles variation in expression, is faster, and allows us to fix identity parameters for a given subject.