The 3rd Grand Challenge of Lightweight 106-Point Facial Landmark Localization on Masked Faces | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Facial landmark localization is a fundamental component in various face related applications, such as face recognition, 3D face reconstruction, facial pose estimation, fa...Show More

Abstract:

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.
Date of Conference: 05-09 July 2021
Date Added to IEEE Xplore: 21 June 2021
ISBN Information:
Conference Location: Shenzhen, China

1. Introduction

Facial landmark localization, also known as face key points detection, is to locate key points of the given face. It can 10-cate a group of points with specific semantic information on 2D face images, such as nose tip, eyebrow curve, mouth corner and so on. Facial landmark localization is a basic step for many face related applications, such as face recognition, 3D face reconstruction, facial expression estimation, etc. In recent years, with the popularity of mobile applications, such as face beauty, the demand for lightweight models is increasing dramatically in both industry and academia. To push the frontier of the lightweight facial landmark localization algorithm, we hosted the 2nd 106-point lightweight Facial Landmark Localization Challenge in 2020. However, due to the global pandemic of COVID-19, people begin to wear facial masks for health and safety. Mask has become an effective way to suppress the transmission of viruses, and the situation will continue in the long term. This apparently makes conventional facial landmark localization unfaithful and inefficient.

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