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arXiv · 1511.04901

Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression

Zhiao Huang·Erjin Zhou·Zhimin Cao
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Abstract

Facial landmark localization plays an important role in face recognition and analysis applications. In this paper, we give a brief introduction to a coarse-to-fine pipeline with neural networks and sequential regression. First, a global convolutional network is applied to the holistic facial image to give an initial landmark prediction. A pyramid of multi-scale local image patches is then cropped to feed to a new network for each landmark to refine the prediction. As the refinement network outputs a more accurate position estimation than the input, such procedure could be repeated several times until the estimation converges. We evaluate our system on the 300-W dataset [11] and it outperforms the recent state-of-the-arts.

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Zhiao Huang, Erjin Zhou, Zhimin Cao. 2015-11-16. Coarse-to-fine Face Alignment with Multi-Scale Local Patch Regression. https://arxiv.org/abs/1511.04901

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