The 3rd Grand Challenge of Lightweight 106-Point Facial Landmark Localization on Masked Faces

Facial landmark localization is a fundamental component in various face related applications, such as face recognition, 3D face reconstruction, facial pose estimation, face synthesis and forensics, etc. However, due to the global pandemic of COVID-19, the facial mask has become a common way to suppress the transmission of the virus. This apparently makes conventional facial landmark localization unfaithful and inefficient because of occlusion. To lead the cutting-edge algorithms on masked faces, we host the 3rd grand challenge of lightweight 106-point facial landmark localization in conjunction with ICME 2021, aiming to improve the accuracy and robustness of facial landmark localization in real-world situations, especially on masked faces. Specifically, we construct a new dataset, named JD-landmark-mask, on the basis of the previous two competitions. It contains about 27,000 face images of two kinds, with real and virtual masks, which are largely varied in identity, head pose, facial expression, and occlusion. Besides, the strict limitations of model size (2M) and computational complexity (100M Flops) are set up for the lightweight model. Finally, more than 80 worldwide universities and research institutes took part in the competition. We will briefly introduce the information of the competition, as well as algorithms and results of the top three teams in this paper.