From ee3d0d98e19f1d8177d85af1866fd0ee431fe9ea Mon Sep 17 00:00:00 2001 From: Jules Laplace Date: Sun, 25 Nov 2018 22:19:15 +0100 Subject: moving stuff --- .../scholar/entries/Robust face landmark estimation under occlusion .csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv (limited to 'scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv') diff --git a/scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv b/scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv new file mode 100644 index 00000000..c1245878 --- /dev/null +++ b/scraper/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv @@ -0,0 +1 @@ +Robust face landmark estimation under occlusion|http://scholar.google.com/https://www.cv-foundation.org/openaccess/content_iccv_2013/html/Burgos-Artizzu_Robust_Face_Landmark_2013_ICCV_paper.html|2013|441|16|6035683787196907858|None|http://scholar.google.com/scholar?cites=6035683787196907858&as_sdt=2005&sciodt=0,5&hl=en|http://scholar.google.com/scholar?cluster=6035683787196907858&hl=en&as_sdt=0,5|None|Human faces captured in real-world conditions present large variations in shape and occlusions due to differences in pose, expression, use of accessories such as sunglasses and hats and interactions with objects (eg food). Current face landmark estimation approaches struggle under such conditions since they fail to provide a principled way of handling outliers. We propose a novel method, called Robust Cascaded Pose Regression (RCPR) which reduces exposure to outliers by detecting occlusions explicitly and using … -- cgit v1.2.3-70-g09d2