From pixels to response maps: discriminative image filtering for face alignment in the wild
We propose a face alignment framework that relies on the texture model generated by the responses of discriminatively trained part-based filters. Unlike standard texture models built from pixel intensities or responses generated by generic filters (e.g. Gabor), our framework has two important advant...
| Main Authors: | , , , , |
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| Format: | Article |
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Institute of Electrical and Electronics Engineers
2014
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| Online Access: | https://eprints.nottingham.ac.uk/31438/ |
| _version_ | 1848794202100465664 |
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| author | Asthana, Akshay Zafeiriou, Stefanos Tzimiropoulos, Georgios Cheng, Shiyang Pantic, Maja |
| author_facet | Asthana, Akshay Zafeiriou, Stefanos Tzimiropoulos, Georgios Cheng, Shiyang Pantic, Maja |
| author_sort | Asthana, Akshay |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | We propose a face alignment framework that relies on the texture model generated by the responses of discriminatively trained part-based filters. Unlike standard texture models built from pixel intensities or responses generated by generic filters (e.g. Gabor), our framework has two important advantages. Firstly, by virtue of discriminative training, invariance to external variations (like identity, pose, illumination and expression) is achieved. Secondly, we show that the responses generated by discriminatively trained filters (or patch-experts) are sparse and can be modeled using a very small number of parameters. As a result, the optimization methods based on the proposed texture model can better cope with unseen variations. We illustrate this point by formulating both part-based and holistic approaches for generic face alignment and show that our framework outperforms the state-of-the-art on multiple ”wild” databases. The code and dataset annotations are available for research purposes from http://ibug.doc.ic.ac.uk/resources. |
| first_indexed | 2025-11-14T19:12:26Z |
| format | Article |
| id | nottingham-31438 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:12:26Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-314382020-05-04T16:56:09Z https://eprints.nottingham.ac.uk/31438/ From pixels to response maps: discriminative image filtering for face alignment in the wild Asthana, Akshay Zafeiriou, Stefanos Tzimiropoulos, Georgios Cheng, Shiyang Pantic, Maja We propose a face alignment framework that relies on the texture model generated by the responses of discriminatively trained part-based filters. Unlike standard texture models built from pixel intensities or responses generated by generic filters (e.g. Gabor), our framework has two important advantages. Firstly, by virtue of discriminative training, invariance to external variations (like identity, pose, illumination and expression) is achieved. Secondly, we show that the responses generated by discriminatively trained filters (or patch-experts) are sparse and can be modeled using a very small number of parameters. As a result, the optimization methods based on the proposed texture model can better cope with unseen variations. We illustrate this point by formulating both part-based and holistic approaches for generic face alignment and show that our framework outperforms the state-of-the-art on multiple ”wild” databases. The code and dataset annotations are available for research purposes from http://ibug.doc.ic.ac.uk/resources. Institute of Electrical and Electronics Engineers 2014-10-09 Article PeerReviewed Asthana, Akshay, Zafeiriou, Stefanos, Tzimiropoulos, Georgios, Cheng, Shiyang and Pantic, Maja (2014) From pixels to response maps: discriminative image filtering for face alignment in the wild. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (6). pp. 1312-1320. ISSN 0162-8828 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6919301&tag=1 doi:10.1109/TPAMI.2014.2362142 doi:10.1109/TPAMI.2014.2362142 |
| spellingShingle | Asthana, Akshay Zafeiriou, Stefanos Tzimiropoulos, Georgios Cheng, Shiyang Pantic, Maja From pixels to response maps: discriminative image filtering for face alignment in the wild |
| title | From pixels to response maps: discriminative image filtering for face alignment in the wild |
| title_full | From pixels to response maps: discriminative image filtering for face alignment in the wild |
| title_fullStr | From pixels to response maps: discriminative image filtering for face alignment in the wild |
| title_full_unstemmed | From pixels to response maps: discriminative image filtering for face alignment in the wild |
| title_short | From pixels to response maps: discriminative image filtering for face alignment in the wild |
| title_sort | from pixels to response maps: discriminative image filtering for face alignment in the wild |
| url | https://eprints.nottingham.ac.uk/31438/ https://eprints.nottingham.ac.uk/31438/ https://eprints.nottingham.ac.uk/31438/ |