Uncover Common Facial Expressions in Terracotta Warriors: A Deep Learning Approach
This provides a technical means for researching Terracotta Warriors art and could inform studies of other ancient arts, but it is incremental as it applies existing deep learning methods to a new domain.
The paper tackled the problem of analyzing facial expressions in Terracotta Warriors, which differ significantly from modern humans, by using Generative Adversarial Networks (GANs) to generate high-quality training data, enabling the identification of common facial expressions for the first time.
Can advanced deep learning technologies be applied to analyze some ancient humanistic arts? Can deep learning technologies be directly applied to special scenes such as facial expression analysis of Terracotta Warriors? The big challenging is that the facial features of the Terracotta Warriors are very different from today's people. We found that it is very poor to directly use the models that have been trained on other classic facial expression datasets to analyze the facial expressions of the Terracotta Warriors. At the same time, the lack of public high-quality facial expression data of the Terracotta Warriors also limits the use of deep learning technologies. Therefore, we firstly use Generative Adversarial Networks (GANs) to generate enough high-quality facial expression data for subsequent training and recognition. We also verify the effectiveness of this approach. For the first time, this paper uses deep learning technologies to find common facial expressions of general and postured Terracotta Warriors. These results will provide an updated technical means for the research of art of the Terracotta Warriors and shine lights on the research of other ancient arts.