论文标题
多标签学习，使用组合的面部动作单元数据集具有缺失值
Multi-label Learning with Missing Values using Combined Facial Action Unit Datasets
论文作者
论文摘要
面部动作单元允许对面部微动作的客观，标准化描述，可用于描述人脸上的情绪。注释动作单元的数据是一项昂贵且耗时的任务，这导致数据情况稀缺。通过将来自不同研究的多个数据集结合在一起，可以增加机器学习算法的培训数据量，以创建可自动化的多标签动作单元检测的强大模型。但是，每项研究都注释不同的动作单元，从而导致组合数据库中大量缺失标签。在这项工作中，我们研究了这一挑战，并提出了创建一个组合数据库和能够在缺失标签的存在下学习而无需推断其价值的算法的方法。与最近的动作单位检测中的比赛相比，我们的方法表现出竞争性能。
Facial action units allow an objective, standardized description of facial micro movements which can be used to describe emotions in human faces. Annotating data for action units is an expensive and time-consuming task, which leads to a scarce data situation. By combining multiple datasets from different studies, the amount of training data for a machine learning algorithm can be increased in order to create robust models for automated, multi-label action unit detection. However, every study annotates different action units, leading to a tremendous amount of missing labels in a combined database. In this work, we examine this challenge and present our approach to create a combined database and an algorithm capable of learning under the presence of missing labels without inferring their values. Our approach shows competitive performance compared to recent competitions in action unit detection.
