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

16 November 2015
Zhiao Huang
Erjin Zhou
Zhimin Cao
    CVBM
ArXiv (abs)PDFHTML
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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