Face Alignment at 3000 FPS via Regressing Local Binary Features

• Published in: 2014 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
• Language: English
• 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
• ISSN: 1063-6919; 1063-6919; 2575-7075
• DOI: 10.1109/CVPR.2014.218

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.