Advances in Deep Learning have made possible reliable landmark tracking of human bodies and faces that can be used for a variety of tasks. We test a recent Computer Vision solution, MediaPipe Holistic (MPH), to find out if its tracking of the facial features is reliable enough for a linguistic analysis of data from sign languages, and compare it to an older solution (OpenFace, OF). We use an existing data set of sentences in Kazakh-Russian Sign Language and a newly created small data set of videos with head tilts and eyebrow movements. We find that MPH does not perform well enough for linguistic analysis of eyebrow movement -- but in a different way from OF, which is also performing poorly without correction. We reiterate a previous proposal to train additional correction models to overcome these limitations.

翻译：深度学习的最新进展使得可用于多种任务的人体及面部地标追踪成为可能。我们测试了近期计算机视觉解决方案MediaPipe Holistic（MPH），以评估其面部特征追踪对手语数据语言学分析的可靠性，并与较早的解决方案OpenFace（OF）进行对比。我们使用了现有的哈萨克-俄罗斯手语句子数据集，以及新创建的小规模头部倾斜与眉毛运动视频数据集。研究发现，MPH对眉毛运动的语言学分析效果不佳——但其不足的方式与OF不同（OF未校正时同样表现欠佳）。我们重申此前提出的建议：需训练额外校正模型以克服这些局限。