Project-out cascaded regression with an application to face alignment
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...
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| Format: | Conference or Workshop Item |
| Published: |
2015
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| Online Access: | https://eprints.nottingham.ac.uk/31442/ |
| _version_ | 1848794203541209088 |
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| author | Tzimiropoulos, Georgios |
| author_facet | Tzimiropoulos, Georgios |
| author_sort | Tzimiropoulos, Georgios |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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 http://www.cs.nott.ac.uk/yzt/ . |
| first_indexed | 2025-11-14T19:12:28Z |
| format | Conference or Workshop Item |
| id | nottingham-31442 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:28Z |
| publishDate | 2015 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314422020-05-04T20:12:04Z https://eprints.nottingham.ac.uk/31442/ Project-out cascaded regression with an application to face alignment Tzimiropoulos, Georgios 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 http://www.cs.nott.ac.uk/yzt/ . 2015 Conference or Workshop Item PeerReviewed 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. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7298989&filter=AND%28p_Publication_Number:7293313%29 |
| spellingShingle | Tzimiropoulos, Georgios Project-out cascaded regression with an application to face alignment |
| title | Project-out cascaded regression with an application to face alignment |
| title_full | Project-out cascaded regression with an application to face alignment |
| title_fullStr | Project-out cascaded regression with an application to face alignment |
| title_full_unstemmed | Project-out cascaded regression with an application to face alignment |
| title_short | Project-out cascaded regression with an application to face alignment |
| title_sort | project-out cascaded regression with an application to face alignment |
| url | https://eprints.nottingham.ac.uk/31442/ https://eprints.nottingham.ac.uk/31442/ |