From 4345e7ba370113c56afbd7e0eda6a1696146a328 Mon Sep 17 00:00:00 2001 From: Jules Date: Tue, 30 Oct 2018 21:14:59 -0400 Subject: data --- .../scholar/entries/Robust face landmark estimation under occlusion .csv | 1 + 1 file changed, 1 insertion(+) create mode 100644 datasets/scholar/entries/Robust face landmark estimation under occlusion .csv (limited to 'datasets/scholar/entries/Robust face landmark estimation under occlusion .csv') diff --git a/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv b/datasets/scholar/entries/Robust face landmark estimation under occlusion .csv new file mode 100644 index 00000000..c1245878 --- /dev/null +++ b/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