Unconstrained Facial Landmark Localization with Backbone-Branches Fully-Convolutional Networks

Understand

This paper investigates how to rapidly and accurately localize facial landmarks in unconstrained, cluttered environments rather than in the well segmented face images.

• We present a novel Backbone-Branches Fully-Convolutional Neural Network (BB-FCN), which produces facial landmark response maps directly from raw images without relying on pre-process or sliding window approaches.
• BB-FCN contains one backbone and a number of network branches with each corresponding to one landmark type, and it operates in a progressive manner.
• Specifically, the backbone roughly detects the locations of facial landmarks by taking the whole image as input, and the branches further refine the localizations based on a local observation from the backbone's intermediate feature map.