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| -rw-r--r-- | scraper/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv | 1 |
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diff --git a/scraper/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv b/scraper/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv new file mode 100644 index 00000000..38f502f9 --- /dev/null +++ b/scraper/datasets/scholar/entries/300 Faces in-the-Wild Challenge: The first facial landmark localization Challenge.csv @@ -0,0 +1 @@ +300 faces in-the-wild challenge: The first facial landmark localization challenge|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|2013|396|15|7861246476672124064|http://openaccess.thecvf.com/content_iccv_workshops_2013/W11/papers/Sagonas_300_Faces_in-the-Wild_2013_ICCV_paper.pdf|http://scholar.google.com/scholar?cites=7861246476672124064&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=7861246476672124064&hl=en&as_sdt=0,5|None|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 … |