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Face Alignment at 3000 FPS via Regressing Local Binary Features
| Citations: | 21 - 1 self |
BibTeX
@MISC{Ren_facealignment,
author = {Shaoqing Ren and Xudong Cao and Yichen Wei and Jian Sun},
title = {Face Alignment at 3000 FPS via Regressing Local Binary Features},
year = {}
}
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Abstract
This paper presents a highly efficient, very accurate re-gression 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 independent-ly. 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 cur-rent most challenging benchmarks. Furthermore, because extracting and regressing local binary features is computa-tionally 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. 1.
Keyphrases
face alignment regressing local binary feature local binary feature locality principle locality principle guide linear regression final output state-of-the-art result facial landmark independent-ly challenging benchmark discriminative local binary feature mobile phone novel component accurate re-gression approach previous method obtained local binary feature
