We present the first public dataset with videos of oro-facial gestures performed by individuals with oro-facial impairment due to neurological disorders, such as amyotrophic lateral sclerosis (ALS) and stroke. Perceptual clinical scores from trained clinicians are provided as metadata. Manual annotation of facial landmarks is also provided for a subset of over 3300 frames. Through extensive experiments with multiple facial landmark detection algorithms, including state-of-the-art convolutional neural network (CNN) models, we demonstrated the presence of bias in the landmark localization accuracy of pre-trained face alignment approaches in our participant groups. The pre-trained models produced higher errors in the two clinical groups compared to age-matched healthy control subjects. We also investigated how this bias changes when the existing models are fine-tuned using data from the target population. The release of this dataset aims to propel the development of face alignment algorithms robust to the presence of oro-facial impairment, support the automatic analysis and recognition of oro-facial gestures, enhance the automatic identification of neurological diseases, as well as the estimation of disease severity from videos and images.

中文翻译：

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神经疾病患者面部运动分析的新数据集

我们展示了第一个公共数据集，其中包含因神经系统疾病（例如肌萎缩侧索硬化症 (ALS) 和中风）而导致口面部损伤的个体所进行的口面部手势视频。来自训练有素的临床医生的感知临床评分作为元数据提供。还为超过 3300 帧的子集提供了面部标志的手动注释。通过对多种面部标志检测算法的广泛实验，包括最先进的卷积神经网络 (CNN) 模型，我们证明了在我们的参与者组中预训练的面部对齐方法的标志定位精度存在偏差。与年龄匹配的健康对照受试者相比，预先训练的模型在两个临床组中产生了更高的错误。我们还研究了当使用来自目标人群的数据对现有模型进行微调时，这种偏差如何变化。该数据集的发布旨在推动对口面部损伤存在鲁棒性的人脸对齐算法的开发，支持口面部手势的自动分析和识别，增强神经系统疾病的自动识别，以及估计来自视频和图像的疾病严重程度。