Face Alignment at 3000 FPS via Regressing Local Binary Features

This paper presents a highly efficient, very accurate regression approach for face alignment. Our approach has two novel components: a set of local binary features, and a locality principle for learning those features. The locality principle guides us to learn a set of highly discriminative local bi...

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Bibliographic Details
Published in2014 IEEE Conference on Computer Vision and Pattern Recognition pp. 1685 - 1692
Main Authors Ren, Shaoqing, Cao, Xudong, Wei, Yichen, Sun, Jian
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.06.2014
Subjects
Alignment
Benchmarks
Computer vision
Conferences
Face
Face Alignment
Facial
Feature extraction
Landmarks
Learning
Linear regression
Pattern recognition
Random Forest
Regression
Shape
Testing
Training
Vegetation
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Summary:This paper presents a highly efficient, very accurate regression approach for face alignment. Our approach has two novel components: a set of local binary features, and 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. Our approach achieves the state-of-the-art results when tested on the current most challenging benchmarks. Furthermore, because extracting and regressing local binary features is computationally very cheap, our system is much faster than previous methods. It achieves over 3, 000 fps on a desktop or 300 fps on a mobile phone for locating a few dozens of landmarks.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
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SourceType-Conference Papers & Proceedings-2
ISSN:1063-6919
1063-6919
2575-7075
DOI:10.1109/CVPR.2014.218