The accurate localization of facial landmarks is at the core of face analysis
tasks, such as face recognition and facial expression analysis, to name a few.
In this work we propose a novel localization approach based on a Deep Learning
architecture that utilizes dual cascaded CNN subnetworks of the same length,
where each subnetwork in a cascade refines the accuracy of its predecessor. The
first set of cascaded subnetworks estimates heatmaps that encode the landmarks'
locations, while the second set of cascaded subnetworks refines the
heatmaps-based localization using regression, and also receives as input the
output of the corresponding heatmap estimation subnetwork. The proposed scheme
is experimentally shown to compare favorably with contemporary state-of-the-art
schemes
