Synergy between face alignment and tracking via Discriminative Global Consensus Optimization

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

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.

Citation

Khan, M. H., McDonagh, J., & Tzimiropoulos, G. Synergy between face alignment and tracking via Discriminative Global Consensus Optimization. Presented at International Conference on Computer Vision (ICCV17)

• Conference Name: International Conference on Computer Vision (ICCV17)
• End Date: Oct 29, 2017
• Acceptance Date: Jul 17, 2017
• Publication Date: Oct 26, 2017
• Deposit Date: Aug 9, 2017
• Publicly Available Date: Oct 26, 2017
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/890025
• Publisher URL: http://ieeexplore.ieee.org/document/8237671/
• Related Public URLs: http://iccv2017.thecvf.com/
• 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.
• Contract Date: Aug 9, 2017