NDLI: Face Flow

Content Provider	IEEE Xplore Digital Library
Author	Snape, Patrick Roussos, Anastasios Panagakis, Yannis Zafeiriou, Stefanos
Copyright Year	2015
Abstract	In this paper, we propose a method for the robust and efficient computation of multi-frame optical flow in an expressive sequence of facial images. We formulate a novel energy minimisation problem for establishing dense correspondences between a neutral template and every frame of a sequence. We exploit the highly correlated nature of human expressions by representing dense facial motion using a deformation basis. Furthermore, we exploit the even higher correlation between deformations in a given input sequence by imposing a low-rank prior on the coefficients of the deformation basis, yielding temporally consistent optical flow. Our proposed model-based formulation, in conjunction with the inverse compositional strategy and low-rank matrix optimisation that we adopt, leads to a highly efficient algorithm for calculating facial flow. As experimental evaluation, we show quantitative experiments on a challenging novel benchmark of face sequences, with dense ground truth optical flow provided by motion capture data. We also provide qualitative results on a real sequence displaying fast motion and occlusions. Extensive quantitative and qualitative comparisons demonstrate that the proposed method outperforms state-of-the-art optical flow and dense non-rigid registration techniques, whilst running an order of magnitude faster.
Starting Page	2993
Ending Page	3001
File Size	2007138
Page Count	9
File Format	PDF
ISSN	23807504
e-ISBN	9781467383912
DOI	10.1109/ICCV.2015.343
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-07
Publisher Place	Chile
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Optical imaging Face Integrated optics Adaptive optics Estimation Optical variables control Robustness
Content Type	Text
Resource Type	Article

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