A functional regression approach to facial landmark tracking

Sánchez-Lozano, Enrique and Tzimiropoulos, Georgios and Martinez, Brais and De la Torre, Fernando and Valstar, Michel (2017) A functional regression approach to facial landmark tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence . ISSN 1939-3539

[img]
Preview
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Download (5MB) | Preview

Abstract

Linear regression is a fundamental building block in many face detection and tracking algorithms, typically used to predict shape displacements from image features through a linear mapping. This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting in the first real-time incremental face tracker. Contrary to prior work in Functional Regression, in which B-splines or Fourier series were used, we propose to approximate the input space by its first-order Taylor expansion, yielding a closed-form solution for the continuous domain of displacements. We then extend the continuous least squares problem to correlated variables, and demonstrate the generalisation of our approach. We incorporate Continuous Regression into the cascaded regression framework, and show its computational benefits for both training and testing. We then present a fast approach for incremental learning within Cascaded Continuous Regression, coined iCCR, and show that its complexity allows real-time face tracking, being 20 times faster than the state of the art. To the best of our knowledge, this is the first incremental face tracker that is shown to operate in real-time. We show that iCCR achieves state-of-the-art performance on the 300-VW dataset, the most recent, large-scale benchmark for face tracking.

Item Type: Article
Additional Information: c) 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works.
Keywords: Continuous Regression, Face Tracking, Functional Regression, Functional Data Analysis
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: 10.1109/TPAMI.2017.2745568
Depositing User: Valstar, Michel
Date Deposited: 07 Sep 2017 14:00
Last Modified: 09 Sep 2017 01:38
URI: http://eprints.nottingham.ac.uk/id/eprint/45491

Actions (Archive Staff Only)

Edit View Edit View