Face alignment by learning from small real datasets and large synthetic datasets

In recent years, face-related research had a wide variety of real-life applications. However, issues such as privacy violations and data abuse caused by its applications have also triggered global controversy. It is undeniable that face-related technology is efficient and convenient, but the dangers and risks are hidden by face technology should be comprehensively considered. Current face algorithms are still challenging in complex and challenging environments (e.g., large angles or expressions). Firstly the existing public training datasets are mostly frontal faces, which have an unbalanced distribution of challenging data. Secondly, the collected real datasets require explicit user consent, and the annotation process is time-consuming and expensive. In this paper, we open a new research direction through synthetic datasets. We try to use synthetic datasets to reduce the dependence of the model on the real-world data set. The face alignment experiments explore the synthetic dataset's complementarity and availability.