Face Alignment by Coarse-to-Fine Shape Searching

Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 4998-5006


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.

Related Material

author = {Zhu, Shizhan and Li, Cheng and Change Loy, Chen and Tang, Xiaoou},
title = {Face Alignment by Coarse-to-Fine Shape Searching},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}