Face Alignment by coarse-to-fine shape searching
Shizhan Zhu1,2, Cheng Li2, Chen Change Loy1,3, Xiaoou Tang1,3
1Department of Information Engineering, The Chinese University of Hong Kong
3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
We present a novel face alignment framework based on coarse-to-fine shape searching. Unlike the conventional cascaded regression approaches that start with an initial shape and refine the shape in a cascaded manner, our approach begins with a coarse search over a shape space that contains diverse shapes, and employs the coarse solution to constrain subsequent finer search of shapes. The unique stage-by-stage progressive and adaptive search i) prevents the final solution from being trapped in local optima due to poor initialisation, a common problem encountered by cascaded regression approaches; and ii) improves the robustness in coping with large pose variations. The framework demonstrates real-time performance and state-of-theart results on various benchmarks including the challenging 300-W dataset.
Our full version codes (open source, including training) is now available at the github page https://github.com/zhusz/CVPR15-CFSS. Please refer to that page for details.
A pre-trained model (for the original paper) can be downloaded from here.
A pre-trained model (trained for Matlab VJ face detection box) can be downloaded from here.
Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang. Face alignment by coarse-to-fine shape searching, in Proceedings of Computer Vision and Pattern Recognition (CVPR), 2015.