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. The aim of this work is not to propose a new expression recognition technique, but to understand better the adequacy of existing state-of-the art optical flow for encoding facial motion in the context of facial expression recognition. Our evaluations highlight the fact that motion approximation methods used to overcome motion discontinuities have a significant impact when optical flows are used to characterize facial expressions.
翻译:光学流技术在估计场景中运动时越来越具有性能和活力,但其性能尚未在面部表情识别领域得到证明。在这项工作中,通过多个面部表情表达数据集对各种光学流方法进行评价,以提供一致的性能评估。这项工作的目的不是提出一种新的表达识别技术,而是更好地了解现有最先进的光学流是否足以在面部表情识别方面对面部运动进行编码。我们的评估突出表明,在使用光学流来描述面部表达时,用于克服运动不连续现象的运动近似方法具有重大影响。