Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/1805.01760
