The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
Face detection, pose estimation, and landmark localization in the wild
Abstract
We present a unified model for face detection, pose estimation, and landmark estimation in real-world, cluttered images. Our model is based on a mixtures of trees with a shared pool of parts; we model every facial landmark as a part and use global mixtures to capture topological changes due to viewpoint. We show that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures. We present extensive results on standard face benchmarks, as well as a new “in the wild” annotated dataset, that suggests our system advances the state-of-the-art, sometimes considerably, for all three tasks. Though our model is modestly trained with hundreds of faces, it compares favorably to commercial systems trained with billions of examples (such as Google Picasa and face.com).
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RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
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A convolutional neural network cascade for face detection
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Metadata
- Authors
- Zhu, X., & Ramanan, D.
- Venue
- 2012 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2012-06
- Pages
- 2879-2886
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2047508432
- Cited by
- 2,120
- References
- 37
- FWCI
- 134.70
- Type
- conference paper
- Language
- en
- Open access
- closed
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