Project-out cascaded regression with an application to face alignment

Tzimiropoulos, Georgios (2015) Project-out cascaded regression with an application to face alignment. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7-12 June 2015, Boston, USA.

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Cascaded regression approaches have been recently shown to achieve state-of-the-art performance for many computer vision tasks. Beyond its connection to boosting, cascaded regression has been interpreted as a learning-based approach to iterative optimization methods like the Newton’s method. However, in prior work, the connection to optimization theory is limited only in learning a mapping from image features to problem parameters. In this paper, we consider the problem of facial deformable model fitting using cascaded regression and make the following contributions: (a) We propose regression to learn a sequence of averaged Jacobian and Hessian matrices from data, and from them descent directions in a fashion inspired by Gauss-Newton optimization. (b) We show that the optimization problem in hand has structure and devise a learning strategy for a cascaded regression approach that takes the problem structure into account. By doing so, the proposed method learns and employs a sequence of averaged Jacobians and descent directions in a subspace orthogonal to the facial appearance variation; hence, we call it Project-Out Cascaded Regression (PO-CR). (c) Based on the principles of PO-CR, we built a face alignment system that produces remarkably accurate results on the challenging iBUG data set outperforming previously proposed systems by a large margin. Code for our system is available from .

Item Type: Conference or Workshop Item (Paper)
Additional Information: © 2015 IEEE. Published in: Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. [Piscataway, N.J.] :IEEE, c2015. ISBN 9781467369640.
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Tzimiropoulos, Yorgos
Date Deposited: 29 Jan 2016 11:28
Last Modified: 04 May 2020 20:12

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