Cascaded regression with sparsified feature covariance matrix for facial landmark detection
This paper explores the use of context on regression-based methods for facial landmarking. Regression based methods have revolutionised facial landmarking solutions. In particular those that implicitly infer the whole shape of a structured object have quickly become the state-of-the-art. The most no...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Published: |
Elsevier
2016
|
| Online Access: | https://eprints.nottingham.ac.uk/31303/ |
| _version_ | 1848794172157329408 |
|---|---|
| author | Sánchez Lozano, Enrique Martinez, Brais Valstar, Michel F. |
| author_facet | Sánchez Lozano, Enrique Martinez, Brais Valstar, Michel F. |
| author_sort | Sánchez Lozano, Enrique |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper explores the use of context on regression-based methods for facial landmarking. Regression based methods have revolutionised facial landmarking solutions. In particular those that implicitly infer the whole shape of a structured object have quickly become the state-of-the-art. The most notable exemplar is the Supervised Descent Method (SDM). Its main characteristics are the use of the cascaded regression approach, the use of the full appearance as the inference input, and the aforementioned aim to directly predict the full shape. In this article we argue that the key aspects responsible for the success of SDM are the use of cascaded regression and the avoidance of the constrained optimisation problem that characterised most of the previous approaches.We show that, surprisingly, it is possible to achieve comparable or superior performance using only landmark-specific predictors, which are linearly combined. We reason that augmenting the input with too much context (of which using the full appearance is the extreme case) can be harmful. In fact, we experimentally found that there is a relation between the data variance and the benefits of adding context to the input. We finally devise a simple greedy procedure that makes use of this fact to obtain superior performance to the SDM, while maintaining the simplicity of the algorithm. We show extensive results both for intermediate stages devised to prove the main aspects of the argumentative line, and to validate the overall performance of two models constructed based on these considerations. |
| first_indexed | 2025-11-14T19:11:58Z |
| format | Article |
| id | nottingham-31303 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:11:58Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-313032020-05-04T17:32:25Z https://eprints.nottingham.ac.uk/31303/ Cascaded regression with sparsified feature covariance matrix for facial landmark detection Sánchez Lozano, Enrique Martinez, Brais Valstar, Michel F. This paper explores the use of context on regression-based methods for facial landmarking. Regression based methods have revolutionised facial landmarking solutions. In particular those that implicitly infer the whole shape of a structured object have quickly become the state-of-the-art. The most notable exemplar is the Supervised Descent Method (SDM). Its main characteristics are the use of the cascaded regression approach, the use of the full appearance as the inference input, and the aforementioned aim to directly predict the full shape. In this article we argue that the key aspects responsible for the success of SDM are the use of cascaded regression and the avoidance of the constrained optimisation problem that characterised most of the previous approaches.We show that, surprisingly, it is possible to achieve comparable or superior performance using only landmark-specific predictors, which are linearly combined. We reason that augmenting the input with too much context (of which using the full appearance is the extreme case) can be harmful. In fact, we experimentally found that there is a relation between the data variance and the benefits of adding context to the input. We finally devise a simple greedy procedure that makes use of this fact to obtain superior performance to the SDM, while maintaining the simplicity of the algorithm. We show extensive results both for intermediate stages devised to prove the main aspects of the argumentative line, and to validate the overall performance of two models constructed based on these considerations. Elsevier 2016-01-13 Article PeerReviewed Sánchez Lozano, Enrique, Martinez, Brais and Valstar, Michel F. (2016) Cascaded regression with sparsified feature covariance matrix for facial landmark detection. Pattern Recognition Letters . ISSN 0167-8655 (In Press) http://www.sciencedirect.com/science/article/pii/S0167865515004006 doi:10.1016/j.patrec.2015.11.014 doi:10.1016/j.patrec.2015.11.014 |
| spellingShingle | Sánchez Lozano, Enrique Martinez, Brais Valstar, Michel F. Cascaded regression with sparsified feature covariance matrix for facial landmark detection |
| title | Cascaded regression with sparsified feature covariance matrix for facial landmark detection |
| title_full | Cascaded regression with sparsified feature covariance matrix for facial landmark detection |
| title_fullStr | Cascaded regression with sparsified feature covariance matrix for facial landmark detection |
| title_full_unstemmed | Cascaded regression with sparsified feature covariance matrix for facial landmark detection |
| title_short | Cascaded regression with sparsified feature covariance matrix for facial landmark detection |
| title_sort | cascaded regression with sparsified feature covariance matrix for facial landmark detection |
| url | https://eprints.nottingham.ac.uk/31303/ https://eprints.nottingham.ac.uk/31303/ https://eprints.nottingham.ac.uk/31303/ |