DeepAI AI Chat
Log In Sign Up

Grand Challenge of 106-Point Facial Landmark Localization

by   Yinglu Liu, et al.

Facial landmark localization is a very crucial step in numerous face related applications, such as face recognition, facial pose estimation, face image synthesis, etc. However, previous competitions on facial landmark localization (i.e., the 300-W, 300-VW and Menpo challenges) aim to predict 68-point landmarks, which are incompetent to depict the structure of facial components. In order to overcome this problem, we construct a challenging dataset, named JD-landmark. Each image is manually annotated with 106-point landmarks. This dataset covers large variations on pose and expression, which brings a lot of difficulties to predict accurate landmarks. We hold a 106-point facial landmark localization competition1 on this dataset in conjunction with IEEE International Conference on Multimedia and Expo (ICME) 2019. The purpose of this competition is to discover effective and robust facial landmark localization approaches.


Face frontalization for Alignment and Recognition

Recently, it was shown that excellent results can be achieved in both fa...

Sub-pixel face landmarks using heatmaps and a bag of tricks

Accurate face landmark localization is an essential part of face recogni...

Multi-spectral Facial Landmark Detection

Thermal face image analysis is favorable for certain circumstances. For ...

Facial Landmark Point Localization using Coarse-to-Fine Deep Recurrent Neural Network

Facial landmark point localization is a typical problem in computer visi...

Joint Voxel and Coordinate Regression for Accurate 3D Facial Landmark Localization

3D face shape is more expressive and viewpoint-consistent than its 2D co...

Improving Landmark Localization with Semi-Supervised Learning

We present two techniques to improve landmark localization from partiall...

Robust Facial Landmark Localization Based on Texture and Pose Correlated Initialization

Robust facial landmark localization remains a challenging task when face...