Facial Landmark Machines: A Backbone-Branches Architecture with Progressive Representation Learning
This work addresses the problem of accurate facial landmark localization for face recognition and analysis applications, presenting an incremental improvement with a novel architecture and a new benchmark.
The paper tackles facial landmark localization in unconstrained settings by proposing a backbone-branches fully convolutional neural network (BB-FCN) that directly generates landmark response maps from raw images, achieving state-of-the-art performance in both constrained and unconstrained evaluations and improving face detection precision and recall.
Facial landmark localization plays a critical role in face recognition and analysis. In this paper, we propose a novel cascaded backbone-branches fully convolutional neural network~(BB-FCN) for rapidly and accurately localizing facial landmarks in unconstrained and cluttered settings. Our proposed BB-FCN generates facial landmark response maps directly from raw images without any preprocessing. BB-FCN follows a coarse-to-fine cascaded pipeline, which consists of a backbone network for roughly detecting the locations of all facial landmarks and one branch network for each type of detected landmark for further refining their locations. Furthermore, to facilitate the facial landmark localization under unconstrained settings, we propose a large-scale benchmark named SYSU16K, which contains 16000 faces with large variations in pose, expression, illumination and resolution. Extensive experimental evaluations demonstrate that our proposed BB-FCN can significantly outperform the state-of-the-art under both constrained (i.e., within detected facial regions only) and unconstrained settings. We further confirm that high-quality facial landmarks localized with our proposed network can also improve the precision and recall of face detection.