TY - GEN
T1 - UGLLI face alignment
T2 - 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
AU - Kumar, Abhinav
AU - Marks, Tim K.
AU - Mou, Wenxuan
AU - Feng, Chen
AU - Liu, Xiaoming
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations. In this paper, we present a novel frame-work for jointly predicting facial landmark locations and the associated uncertainties, modeled as 2D Gaussian distributions, using Gaussian log-likelihood loss. Not only does our joint estimation of uncertainty and landmark locations yield state-of-the-art estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations (face alignment). Our method's estimates of the uncertainty of landmarks' predicted locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.
AB - Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations. In this paper, we present a novel frame-work for jointly predicting facial landmark locations and the associated uncertainties, modeled as 2D Gaussian distributions, using Gaussian log-likelihood loss. Not only does our joint estimation of uncertainty and landmark locations yield state-of-the-art estimates of the uncertainty of predicted landmark locations, but it also yields state-of-the-art estimates for the landmark locations (face alignment). Our method's estimates of the uncertainty of landmarks' predicted locations could be used to automatically identify input images on which face alignment fails, which can be critical for downstream tasks.
KW - Face alignment
KW - Gaussian log likelihood loss
KW - Uncertainty estimation
UR - http://www.scopus.com/inward/record.url?scp=85082494198&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082494198&partnerID=8YFLogxK
U2 - 10.1109/ICCVW.2019.00103
DO - 10.1109/ICCVW.2019.00103
M3 - Conference contribution
AN - SCOPUS:85082494198
T3 - Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
SP - 778
EP - 782
BT - Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 October 2019 through 28 October 2019
ER -