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      A Comprehensive Performance Evaluation of Deformable Face Tracking “In-the-Wild”

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          Abstract

          Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as “in-the-wild”). This is partially attributed to the fact that comprehensive “in-the-wild” benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking “in-the-wild”. Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300 VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.

          Electronic supplementary material

          The online version of this article (doi:10.1007/s11263-017-0999-5) contains supplementary material, which is available to authorized users.

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                Author and article information

                Contributors
                g.chrysos@imperial.ac.uk
                e.antonakos@imperial.ac.uk
                p.snape@imperial.ac.uk
                akshay.asthana@seeingmachines.com
                s.zafeiriou@imperial.ac.uk
                Journal
                Int J Comput Vis
                Int J Comput Vis
                International Journal of Computer Vision
                Springer US (New York )
                0920-5691
                25 February 2017
                25 February 2017
                2018
                : 126
                : 2
                : 198-232
                Affiliations
                [1 ]ISNI 0000 0001 2113 8111, GRID grid.7445.2, Department of Computing, , Imperial College London, ; 180 Queen’s Gate, London, SW7 2AZ UK
                [2 ]Seeing Machines Ltd., Level 1, 11 Lonsdale St, Braddon, ACT 2612 Australia
                [3 ]ISNI 0000 0001 0941 4873, GRID grid.10858.34, Center for Machine Vision and Signal Analysis, , University of Oulu, ; Oulu, Finland
                Author notes

                Communicated by Lourdes Agapito.

                Author information
                http://orcid.org/0000-0002-0650-1856
                Article
                999
                10.1007/s11263-017-0999-5
                6953975
                31983805
                f8845e6d-944b-4d80-8d73-08e9048c0769
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 15 March 2016
                : 10 February 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000266, Engineering and Physical Sciences Research Council;
                Award ID: EP/J017787/1
                Award Recipient :
                Funded by: Engineering and Physical Sciences Research Council (GB)
                Award ID: EP/L026813/1
                Award Recipient :
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                © Springer Science+Business Media, LLC, part of Springer Nature 2018

                deformable face tracking,face detection,model free tracking,facial landmark localisation,long-term tracking

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