Abstract
Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are evaluated across multiple facial expression datasets, so as to provide a consistent performance evaluation. Additionally, the strengths of multiple optical flow approaches are combined in a novel data augmentation scheme. Under this scheme, increases in average accuracy of up to 6% (depending on the choice of optical flow approaches and dataset) have been achieved.
Abstract (translated)
在估计场景中的运动时，光流技术正变得越来越具有性能和鲁棒性，但它们的性能还没有在面部表情识别领域得到证实。在这项工作中，各种光流方法被评估跨多个面部表情数据集，以提供一致的性能评估。此外，多光流方法的优点结合在一个新的数据增强方案中。在该方案下，平均精度提高了6%（取决于光流方法和数据集的选择）。
URL
https://arxiv.org/abs/1904.11592
