A SPARSE AND LOW-RANK APPROACH TO EFFICIENT FACE ALIGNMENT FOR PHOTO-REAL TALKING HEAD SYNTHESIS

In this paper, we propose a framework for practical large-scale face

alignment, based on the recent development of Robust Alignment by

Sparse and Low-rank Decomposition for linearly correlated images

(RASL). Unfortunately, the original implementation is not applicable

in large image dataset. We extend this technique to deal with the

situation with millions of images, with the aid of l1-regularized least

squares. Our proposal is applied onto the photo-real talking head, a

challenging application which requires highly precise alignments of

faces from video sequences. We verify the efficacy of our algorithm

with experiments using real talking head data. Our method attains

comparable quality to RASL in the experiments.

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In  ICASSP 2011

Publisher  IEEE

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TypeInproceedings
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