The presence of occluders significantly impacts object recognition accuracy.
However, occlusion is typically treated as an unstructured source of noise and
explicit models for occluders have lagged behind those for object appearance
and shape. In this paper we describe a hierarchical deformable part model for
face detection and landmark localization that explicitly models part occlusion.
The proposed model structure makes it possible to augment positive training
data with large numbers of synthetically occluded instances. This allows us to
easily incorporate the statistics of occlusion patterns in a discriminatively
trained model. We test the model on several benchmarks for landmark
localization and detection including challenging new data sets featuring
significant occlusion. We find that the addition of an explicit occlusion model
yields a detection system that outperforms existing approaches for occluded
instances while maintaining competitive accuracy in detection and landmark
localization for unoccluded instances.

本文提出了一种分层可变形部件模型，用于面部检测和地标定位，能够明确地模拟部件遮挡，这允许我们使用大量的合成遮挡样本数据，以灵活地整合遮挡模式的统计，从而使检测系统在顶部准确性方面胜过传统系统，同时在未遮挡的情况下保持了竞争力。