Unconstrained Face Alignment Without Face Detection

Xiaohu Shao, Junliang Xing, Jiangjing Lv, Chunlin Xiao, Pengcheng Liu, Youji Feng, Cheng Cheng; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 123-131


This paper introduces our submission to the 2nd Facial Landmark Localisation Competition. We present a deep architecture to directly detect facial landmarks without using face detection as an initialization. The architecture consists of two stages, a Basic Landmark Prediction Stage and a Whole Landmark Regression Stage. At the former stage, given an input image, the basic landmarks of all faces are detected by a sub-network of landmark heatmap and affinity field prediction. At the latter stage, the coarse canonical face and the pose can be generated by a Pose Splitting Layer based on the visible basic landmarks. According to its pose, each canonical state is distributed to the corresponding branch of the shape regression sub-networks for the whole landmark detection. Experimental results show that our method obtains promising results on the 300-W dataset, and achieves superior performances over the baselines of the semi-frontal and the profile categories in this competition.

Related Material

author = {Shao, Xiaohu and Xing, Junliang and Lv, Jiangjing and Xiao, Chunlin and Liu, Pengcheng and Feng, Youji and Cheng, Cheng},
title = {Unconstrained Face Alignment Without Face Detection},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}