Active orientation models for face alignment in-the-wild
We present Active Orientation Models (AOMs), generative models of facial shape and appearance, which extend the well-known paradigm of Active Appearance Models (AAMs) for the case of generic face alignment under unconstrained conditions. Robustness stems from the fact that the proposed AOMs employ a...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Published: |
IEEE
2014
|
| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/31437/ |
| _version_ | 1848794201830981632 |
|---|---|
| author | Tzimiropoulos, Georgios Medina, Joan Alabort Zafeiriou, Stefanos Pantic, Maja |
| author_facet | Tzimiropoulos, Georgios Medina, Joan Alabort Zafeiriou, Stefanos Pantic, Maja |
| author_sort | Tzimiropoulos, Georgios |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We present Active Orientation Models (AOMs), generative models of facial shape and appearance, which extend the well-known paradigm of Active Appearance Models (AAMs) for the case of generic face alignment under unconstrained conditions. Robustness stems from the fact that the proposed AOMs employ a statistically robust appearance model based on the principal components of image gradient orientations. We show that when incorporated within standard optimization frameworks for AAM learning and fitting, this kernel Principal Component Analysis results in robust algorithms for model fitting. At the same time, the resulting optimization problems maintain the same computational cost. As a result, the main similarity of AOMs with AAMs is the computational complexity. In particular, the project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm, which is admittedly one of the fastest algorithms for fitting AAMs. We verify experimentally that: 1) AOMs generalize well to unseen variations and 2) outperform all other state-of-the-art AAM methods considered by a large margin. This performance improvement brings AOMs at least in par with other contemporary methods for face alignment. Finally, we provide MATLAB code at http://ibug.doc.ic.ac.uk/resources. |
| first_indexed | 2025-11-14T19:12:26Z |
| format | Article |
| id | nottingham-31437 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:26Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314372020-05-04T16:57:36Z https://eprints.nottingham.ac.uk/31437/ Active orientation models for face alignment in-the-wild Tzimiropoulos, Georgios Medina, Joan Alabort Zafeiriou, Stefanos Pantic, Maja We present Active Orientation Models (AOMs), generative models of facial shape and appearance, which extend the well-known paradigm of Active Appearance Models (AAMs) for the case of generic face alignment under unconstrained conditions. Robustness stems from the fact that the proposed AOMs employ a statistically robust appearance model based on the principal components of image gradient orientations. We show that when incorporated within standard optimization frameworks for AAM learning and fitting, this kernel Principal Component Analysis results in robust algorithms for model fitting. At the same time, the resulting optimization problems maintain the same computational cost. As a result, the main similarity of AOMs with AAMs is the computational complexity. In particular, the project-out version of AOMs is as computationally efficient as the standard project-out inverse compositional algorithm, which is admittedly one of the fastest algorithms for fitting AAMs. We verify experimentally that: 1) AOMs generalize well to unseen variations and 2) outperform all other state-of-the-art AAM methods considered by a large margin. This performance improvement brings AOMs at least in par with other contemporary methods for face alignment. Finally, we provide MATLAB code at http://ibug.doc.ic.ac.uk/resources. IEEE 2014-11-11 Article PeerReviewed Tzimiropoulos, Georgios, Medina, Joan Alabort, Zafeiriou, Stefanos and Pantic, Maja (2014) Active orientation models for face alignment in-the-wild. IEEE Transactions on Information Forensics and Security, 9 (12). pp. 2024-2034. ISSN 1556-6013 Computational Complexity Face Recognition Optimisation Principal Component Analysis http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6914605 doi:10.1109/TIFS.2014.2361018 doi:10.1109/TIFS.2014.2361018 |
| spellingShingle | Computational Complexity Face Recognition Optimisation Principal Component Analysis Tzimiropoulos, Georgios Medina, Joan Alabort Zafeiriou, Stefanos Pantic, Maja Active orientation models for face alignment in-the-wild |
| title | Active orientation models for face alignment in-the-wild |
| title_full | Active orientation models for face alignment in-the-wild |
| title_fullStr | Active orientation models for face alignment in-the-wild |
| title_full_unstemmed | Active orientation models for face alignment in-the-wild |
| title_short | Active orientation models for face alignment in-the-wild |
| title_sort | active orientation models for face alignment in-the-wild |
| topic | Computational Complexity Face Recognition Optimisation Principal Component Analysis |
| url | https://eprints.nottingham.ac.uk/31437/ https://eprints.nottingham.ac.uk/31437/ https://eprints.nottingham.ac.uk/31437/ |