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Synergy between face alignment and tracking via Discriminative Global Consensus Optimization

Khan, Muhammad Haris; McDonagh, John; Tzimiropoulos, Georgios

Authors

Muhammad Haris Khan Muhammad.Khan3@nottingham.ac.uk

John McDonagh

Georgios Tzimiropoulos



Abstract

An open question in facial landmark localization in video is whether one should perform tracking or tracking-by-detection (i.e. face alignment). Tracking produces fittings of high accuracy but is prone to drifting. Tracking-by-detection is drift-free but results in low accuracy fittings. To provide a solution to this problem, we describe the very first, to the best of our knowledge, synergistic approach between detection (face alignment) and tracking which completely eliminates drifting from face tracking, and does not merely perform tracking-by-detection. Our first main contribution is to show that one can achieve this synergy between detection and tracking using a principled optimization framework based on the theory of Global Variable Consensus Optimization using ADMM; Our second contribution is to show how the proposed analytic framework can be integrated within state-of-the-art discriminative methods for face alignment and tracking based on cascaded regression and deeply learned features. Overall, we call our method Discriminative Global Consensus Model (DGCM). Our third contribution is to show that DGCM achieves large performance improvement over the currently best performing face tracking methods on the most challenging category of the 300-VW dataset.

Publication Date Oct 26, 2017
Peer Reviewed Peer Reviewed
APA6 Citation Khan, M. H., McDonagh, J., & Tzimiropoulos, G. (2017). Synergy between face alignment and tracking via Discriminative Global Consensus Optimization
Publisher URL http://ieeexplore.ieee.org/document/8237671/
Related Public URLs http://iccv2017.thecvf.com/
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingh.../end_user_agreement.pdf
Additional Information © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Published in 2017 IEEE International Conference
on Computer Vision ISBN 9781538610329.

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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