Open AccessFace Alignment by Supervised Descent Method with Head Pose Estimation
Author(s) -
Zheng Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1438/1/012018
Subject(s) - computer science , pose , artificial intelligence , face (sociological concept) , key (lock) , computer vision , head (geology) , set (abstract data type) , variation (astronomy) , pattern recognition (psychology) , gradient descent , social science , sociology , physics , computer security , geomorphology , astrophysics , artificial neural network , programming language , geology
Face alignment, which aims at locating facial key points automatically, is an important topic in computer vision community. And many works have been done to solve this problem. The most well-known solution is Supervised Decent Method(SDM). However, SDM has been designed to use mean shape as initial shape, which is vulnerable to large pose variation. In this paper, we present a novel approach for detection of the facial key points, getting initial shape from a special shape according to the head pose of the data. Experiments show that our approach achieves significant improvement. In both 21 points and 68 points detection cases, our method achieves nearly 50% improvement on challenging dataset IBUG, and about 1% improvement in HELEN and LFPW test set.