The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Incremental Face Alignment in the Wild
Open accessAbstract
The development of facial databases with an abundance of annotated facial data captured under unconstrained 'in-the-wild' conditions have made discriminative facial deformable models the de facto choice for generic facial landmark localization. Even though very good performance for the facial landmark localization has been shown by many recently proposed discriminative techniques, when it comes to the applications that require excellent accuracy, such as facial behaviour analysis and facial motion capture, the semi-automatic person-specific or even tedious manual tracking is still the preferred choice. One way to construct a person-specific model automatically is through incremental updating of the generic model. This paper deals with the problem of updating a discriminative facial deformable model, a problem that has not been thoroughly studied in the literature. In particular, we study for the first time, to the best of our knowledge, the strategies to update a discriminative model that is trained by a cascade of regressors. We propose very efficient strategies to update the model and we show that is possible to automatically construct robust discriminative person and imaging condition specific models 'in-the-wild' that outperform state-of-the-art generic face alignment strategies.
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Metadata
- Authors
- Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 1859-1866
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2121684305
- Cited by
- 450
- References
- 35
- FWCI
- 38.02
- Type
- conference paper
- Language
- en
- Open access
- green
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