Grand Challenge of 106-Point Facial Landmark Localization

TL;DR

This paper introduces a new challenging dataset with 106 facial landmarks, aiming to improve facial landmark localization methods beyond traditional 68-point models, and reports on a related competition to evaluate approaches.

Contribution

The creation of the JD-landmark dataset with 106 landmarks and the organization of a competition to advance facial landmark localization techniques.

Findings

Dataset covers large pose and expression variations

Competition results highlight effective localization approaches

Benchmark establishes new challenges for future research

Abstract

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.