Description: We propose a novel method, called Robust
Cascaded Pose Regression (RCPR) which reduces exposure
to outliers by detecting occlusions explicitly and using robust shape-indexed features. We show that RCPR improves
on previous landmark estimation methods on three popular face datasets (LFPW, LFW and HELEN). We further
explore RCPR’s performance by introducing a novel face
dataset focused on occlusion, composed of 1,007 faces presenting a wide range of occlusion patterns. RCPR reduces
failure cases by half on all four datasets, at the same time as
it detects face occlusions with a 80/40 precision/recall.
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Burgos-Artizzu_Robust_Face_Landmark_2013_ICCV_paper.pdf
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