Parra-Dominguez Gemma S, Sanchez-Yanez Raul E, Garcia-Capulin Carlos H
Department of Electronics Engineering, Universidad de Guanajuato DICIS, Salamanca 36885, Mexico.
Healthcare (Basel). 2022 Mar 31;10(4):659. doi: 10.3390/healthcare10040659.
Humans express their emotions verbally and through actions, and hence emotions play a fundamental role in facial expressions and body gestures. Facial expression recognition is a popular topic in security, healthcare, entertainment, advertisement, education, and robotics. Detecting facial expressions via gesture recognition is a complex and challenging problem, especially in persons who suffer face impairments, such as patients with facial paralysis. Facial palsy or paralysis refers to the incapacity to move the facial muscles on one or both sides of the face. This work proposes a methodology based on neural networks and handcrafted features to recognize six gestures in patients with facial palsy. The proposed facial palsy gesture recognition system is designed and evaluated on a publicly available database with good results as a first attempt to perform this task in the medical field. We conclude that, to recognize facial gestures in patients with facial paralysis, the severity of the damage has to be considered because paralyzed organs exhibit different behavior than do healthy ones, and any recognition system must be capable of discerning these behaviors.
人类通过言语和行动来表达情感，因此情感在面部表情和身体姿势中起着至关重要的作用。面部表情识别在安全、医疗保健、娱乐、广告、教育和机器人技术等领域是一个热门话题。通过手势识别来检测面部表情是一个复杂且具有挑战性的问题，尤其是在面部有损伤的人群中，比如面瘫患者。面瘫是指面部一侧或双侧的面部肌肉无法活动。这项工作提出了一种基于神经网络和手工特征的方法，用于识别面瘫患者的六种手势。所提出的面瘫手势识别系统是在一个公开可用的数据库上进行设计和评估的，作为在医学领域执行这项任务的首次尝试，取得了良好的结果。我们得出结论，为了识别面瘫患者的面部手势，必须考虑损伤的严重程度，因为瘫痪的器官表现出与健康器官不同的行为，并且任何识别系统都必须能够辨别这些行为。
