Towards robust cascaded regression for face alignment in the wild

2015 ◽  
Author(s):  
Chengchao Qu ◽  
Hua Gao ◽  
Eduardo Monari ◽  
Jurgen Beyerer ◽  
Jean-Philippe Thiran
Keyword(s):  
Face Alignment ◽  
In The Wild ◽  
Cascaded Regression

scholarly journals The First 3D Face Alignment in the Wild (3DFAW) Challenge

2016 ◽  
pp. 511-520 ◽  
Author(s):  
László A. Jeni ◽  
Sergey Tulyakov ◽  
Lijun Yin ◽  
Nicu Sebe ◽  
Jeffrey F. Cohn
Keyword(s):  
Face Alignment ◽  
3D Face ◽  
In The Wild

scholarly journals Incremental Face Alignment in the Wild

2014 ◽  
Author(s):  
Akshay Asthana ◽  
Stefanos Zafeiriou ◽  
Shiyang Cheng ◽  
Maja Pantic
Keyword(s):  
Face Alignment ◽  
In The Wild

Robust face alignment by cascaded regression and de-occlusion

2020 ◽  
Vol 123 ◽  
pp. 261-272 ◽  
Author(s):  
Jun Wan ◽  
Jing Li ◽  
Zhihui Lai ◽  
Bo Du ◽  
Lefei Zhang
Keyword(s):  
Face Alignment ◽  
Cascaded Regression

The 2nd 3D Face Alignment in the Wild Challenge (3DFAW-Video): Dense Reconstruction From Video

2019 ◽  
Author(s):  
Rohith Krishnan Pillai ◽  
Laszlo Attila Jeni ◽  
Huiyuan Yang ◽  
Zheng Zhang ◽  
Lijun Yin ◽  
...  
Keyword(s):  
Face Alignment ◽  
3D Face ◽  
In The Wild ◽  
Dense Reconstruction

scholarly journals Simultaneous multi-descent regression and feature learning for facial landmarking in depth images

2019 ◽  
Vol 32 (24) ◽  
pp. 17909-17926 ◽  
Author(s):  
Janez Križaj ◽  
Peter Peer ◽  
Vitomir Štruc ◽  
Simon Dobrišek
Keyword(s):  
State Of The Art ◽  
Feature Learning ◽  
Limited Range ◽  
Face Alignment ◽  
Classification Problems ◽  
3D Face ◽  
Face Expression ◽  
Gating Mechanism ◽  
Wide Range ◽  
Cascaded Regression

AbstractFace alignment (or facial landmarking) is an important task in many face-related applications, ranging from registration, tracking, and animation to higher-level classification problems such as face, expression, or attribute recognition. While several solutions have been presented in the literature for this task so far, reliably locating salient facial features across a wide range of posses still remains challenging. To address this issue, we propose in this paper a novel method for automatic facial landmark localization in 3D face data designed specifically to address appearance variability caused by significant pose variations. Our method builds on recent cascaded regression-based methods to facial landmarking and uses a gating mechanism to incorporate multiple linear cascaded regression models each trained for a limited range of poses into a single powerful landmarking model capable of processing arbitrary-posed input data. We develop two distinct approaches around the proposed gating mechanism: (1) the first uses a gated multiple ridge descent mechanism in conjunction with established (hand-crafted) histogram of gradients features for face alignment and achieves state-of-the-art landmarking performance across a wide range of facial poses and (2) the second simultaneously learns multiple-descent directions as well as binary features that are optimal for the alignment tasks and in addition to competitive landmarking results also ensures extremely rapid processing. We evaluate both approaches in rigorous experiments on several popular datasets of 3D face images, i.e., the FRGCv2 and Bosphorus 3D face datasets and image collections F and G from the University of Notre Dame. The results of our evaluation show that both approaches compare favorably to the state-of-the-art, while exhibiting considerable robustness to pose variations.

scholarly journals From Pixels to Response Maps: Discriminative Image Filtering for Face Alignment in the Wild

2015 ◽  
Vol 37 (6) ◽  
pp. 1312-1320 ◽  
Author(s):  
Akshay Asthana ◽  
Stefanos Zafeiriou ◽  
Georgios Tzimiropoulos ◽  
Shiyang Cheng ◽  
Maja Pantic
Keyword(s):  
Image Filtering ◽  
Face Alignment ◽  
In The Wild
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