TY - GEN
T1 - Dense facial landmark localization
T2 - 34rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2019
AU - Ju, Lei
AU - Cui, Xinfang
AU - Zhu, Xiangyu
AU - Wankou, Yang
AU - Zhen, Lei
AU - Changyin, Sun
N1 - Publisher Copyright:
© 2019 IEEE
PY - 2019/6
Y1 - 2019/6
N2 - Databases are of great significance to researchers to achieve a satisfactory model. The lack of data is always a bottleneck to facial landmark localization, especially for the dense facial landmark detection. In this article, we provide a new dataset, called Dense Landmark Localization (DLL) database, which contains 39,198 images and is annotated in high quality. Annotating dense landmarks is a very tedious work due to two challenges. (a) Not every facial point has clear definition. Some of them distribute uniformly along the contour. Their labelled positions are determined by subjective judgement of the annotators, so that the quality of the annotation is poor. (b) Adjusting facial points one by one is time-consuming. The workload will increase dramatically when there are more points. To overcome the aforementioned problems, we propose a semiautomatic annotation tool to annotate dense points with much less clicks.
AB - Databases are of great significance to researchers to achieve a satisfactory model. The lack of data is always a bottleneck to facial landmark localization, especially for the dense facial landmark detection. In this article, we provide a new dataset, called Dense Landmark Localization (DLL) database, which contains 39,198 images and is annotated in high quality. Annotating dense landmarks is a very tedious work due to two challenges. (a) Not every facial point has clear definition. Some of them distribute uniformly along the contour. Their labelled positions are determined by subjective judgement of the annotators, so that the quality of the annotation is poor. (b) Adjusting facial points one by one is time-consuming. The workload will increase dramatically when there are more points. To overcome the aforementioned problems, we propose a semiautomatic annotation tool to annotate dense points with much less clicks.
KW - Landmark localizationdatabasesemi-automatic annotation tool
UR - https://www.scopus.com/pages/publications/85071392146
U2 - 10.1109/YAC.2019.8787631
DO - 10.1109/YAC.2019.8787631
M3 - 会议稿件
AN - SCOPUS:85071392146
T3 - Proceedings - 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2019
SP - 625
EP - 630
BT - Proceedings - 2019 34rd Youth Academic Annual Conference of Chinese Association of Automation, YAC 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 6 June 2019 through 8 June 2019
ER -