The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
Open accessAbstract
Automatic facial point detection plays arguably the most important role in face analysis. Several methods have been proposed which reported their results on databases of both constrained and unconstrained conditions. Most of these databases provide annotations with different mark-ups and in some cases the are problems related to the accuracy of the fiducial points. The aforementioned issues as well as the lack of a evaluation protocol makes it difficult to compare performance between different systems. In this paper, we present the 300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge which is held in conjunction with the International Conference on Computer Vision 2013, Sydney, Australia. The main goal of this challenge is to compare the performance of different methods on a new-collected dataset using the same evaluation protocol and the same mark-up and hence to develop the first standardized benchmark for facial landmark localization.
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Cites
Constrained Local Neural Fields for Robust Facial Landmark Detection in the Wild
Baltrusaitis, T., Robinson, P., & Morency, L.-P. (2013) · 2013 IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2013.54 · Cited by 415
Cited by
Bulat, A., & Tzimiropoulos, G. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.116 · Cited by 1,464
Self-citationRanjan, R., Patel, V. M., & Chellappa, R. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2781233 · Cited by 1,333
300 Faces In-The-Wild Challenge: Database and results
Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2016) · Image and Vision Computing · doi:10.1016/j.imavis.2016.01.002 · Cited by 638
Self-citationTowards Fast, Accurate and Stable 3D Dense Face Alignment
Guo, J., Zhu, X., Yang, Y., Yang, F., Lei, Z., & Li, S. Z. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58529-7_10 · Cited by 462
Deep Alignment Network: A Convolutional Neural Network for Robust Face Alignment
Kowalski, M., Naruniec, J., & Trzcinski, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.254 · Cited by 336
Metadata
- Authors
- Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M.
- Venue
- 2013 IEEE International Conference on Computer Vision Workshops
- Publisher
- IEEE
- Published
- 2013-12
- Pages
- 397-403
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2058961190
- Cited by
- 1,119
- References
- 1
- FWCI
- 38.40
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013). 300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge. 2013 IEEE International Conference on Computer Vision Workshops. https://doi.org/10.1109/iccvw.2013.59
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