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Author Shaoqing Ren ♦ Xudong Cao ♦ Yichen Wei ♦ Jian Sun
Sponsorship IEEE Signal Processing Society
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©1992
Language English
Subject Domain (in DDC) Natural sciences & mathematics ♦ Physics ♦ Electricity & electronics
Subject Keyword Face ♦ Shape ♦ Vegetation ♦ Detectors ♦ Feature extraction ♦ Regression tree analysis ♦ Training ♦ local binary feature ♦ face alignment ♦ tracking ♦ random forest ♦ local binary feature ♦ Face alignment ♦ tracking ♦ random forest
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 up to 16% relatively. To facilitate practical usage of face detection/alignment methods, we also propose a convenient metric to measure how good a detector is for alignment initialization.
Description Author affiliation :: Visual Comput. Group, Microsoft Res., Beijing, China
Author affiliation :: Univ. of Sci. & Technol. of China, Hefei, China
Author affiliation :: Microsoft Res., Beijing, China
ISSN 10577149
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2016-01-01
Publisher Place U.S.A.
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Volume Number 25
Issue Number 3
Size (in Bytes) 4.34 MB
Page Count 13
Starting Page 1233
Ending Page 1245

Source: IEEE Xplore Digital Library