Face Alignment via Regressing Local Binary Features

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Shaoqing RenJian Sun

Abstract

This paper presents a highly efficient and accurate regression approach for face alignment. Our approach has two novel components: 1) a set of local binary features and 2) a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local binary features for each facial landmark independently. The obtained local binary features are used to jointly learn a linear regression for the final output. This approach achieves the state-of-the-art results when tested on the most challenging benchmarks to date. Furthermore, because extracting and regressing local binary features are computationally very cheap, our system is much faster than previous methods. It achieves over 3000 frames per second (FPS) on a desktop or 300 FPS on a mobile phone for locating a few dozens of landmarks. We also study a key issue that is important but has received little attention in the previous research, which is the face detector used to initialize alignment. We investigate several face detectors and perform quantitative evaluation on how they affect alignment accuracy. We find that an alignment friendly detector can further greatly boost the accuracy of our alignment method, reducing the error ...Continue Reading

References

Jun 23, 2008·Proceedings·Yang WangJeffrey F Cohn

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Citations

Dec 4, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Qingshan Liu Dacheng Tao
Feb 13, 2018·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Jiajian ZengGang Wang
Jun 30, 2019·Neural Networks : the Official Journal of the International Neural Network Society·Meilu ZhuJunbin Gao

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