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diff --git a/scraper/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv b/scraper/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv new file mode 100644 index 00000000..eaaf1a93 --- /dev/null +++ b/scraper/datasets/scholar/entries/300 faces In-the-wild challenge: Database and results.csv @@ -0,0 +1 @@ +300 faces in-the-wild challenge: Database and results|http://scholar.google.com/https://www.sciencedirect.com/science/article/pii/S0262885616000147|2016|141|9|4741451765657920988|None|http://scholar.google.com/scholar?cites=4741451765657920988&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=4741451765657920988&hl=en&as_sdt=0,5|None|Computer Vision has recently witnessed great research advance towards automatic facial points detection. Numerous methodologies have been proposed during the last few years that achieve accurate and efficient performance. However, fair comparison between these methodologies is infeasible mainly due to two issues.(a) Most existing databases, captured under both constrained and unconstrained (in-the-wild) conditions have been annotated using different mark-ups and, in most cases, the accuracy of the annotations is low.(b) Most … |