  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 cascaded subnetworks with convolutional neural
network units. The cascaded units of the first subnetwork estimate
heatmap-based encodings of the landmarks locations, while the cascaded units of
the second subnetwork receive as input the output of the corresponding heatmap
estimation units, and refine them through regression. The proposed scheme is
experimentally shown to compare favorably with contemporary state-of-the-art
schemes, especially when applied to images depicting challenging localization
conditions.
