Abstract
Facial landmark localization has been applied to numerous face related applications, such as face recognition and face image synthesis. It is a very crucial step for achieving high performance in these applications. We host the \(2^\mathrm{nd}\) 106-point lightweight facial landmark localization grand challenge in conjunction with ICPR 2020. The purpose is to make effort towards benchmarking lightweight facial landmark localization, which enables efficient system deployment. Compared with the \(1^\mathrm{st}\) grand challenge (https://facial-landmarks-localization-challenge.github.io/), the JD-landmark-v2 dataset contains more than 24,000 images with larger variations in identity, pose, expression and occlusion. Besides, strict limits of model size (\(\le \)20M) and computational complexity (\(\le \)1G Flops) are employed for computational efficiency. The challenge has attracted attention from academia and industrial practitioners. More than 70 teams participate in the competition, and nine of them involve in the final evaluation. We give a detailed introduction of the competition and the solution by the winners in this paper.
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Acknowledgment
This work was supported by the National Key R&D Program of China under Grant No. 2020AAA0103800.
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Liu, Y. et al. (2021). The \(2^\mathrm{nd}\) 106-Point Lightweight Facial Landmark Localization Grand Challenge. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12668. Springer, Cham. https://doi.org/10.1007/978-3-030-68793-9_25
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