Analyzing facial features and expressions is a complex task in computer vision. The human face is intricate, with significant shape, texture, and appearance variations. In medical contexts, facial structures that differ from the norm, such as those affected by paralysis, are particularly important to study and require precise analysis. One area of interest is the subtle movements involved in blinking, a process that is not yet fully understood and needs high-resolution, time-specific analysis for detailed understanding. However, a significant challenge is that many advanced computer vision techniques demand programming skills, making them less accessible to medical professionals who may not have these skills. The Jena Facial Palsy Toolbox (JeFaPaTo) has been developed to bridge this gap. It utilizes cutting-edge computer vision algorithms and offers a user-friendly interface for those without programming expertise. This toolbox is designed to make advanced facial analysis more accessible to medical experts, simplifying integration into their workflow. The state of the eye closure is of high interest to medical experts, e.g., in the context of facial palsy or Parkinson's disease. Due to facial nerve damage, the eye-closing process might be impaired and could lead to many undesirable side effects. Hence, more than a simple distinction between open and closed eyes is required for a detailed analysis. Factors such as duration, synchronicity, velocity, complete closure, the time between blinks, and frequency over time are highly relevant. Such detailed analysis could help medical experts better understand the blinking process, its deviations, and possible treatments for better eye care.

翻译：面部特征与表情分析是计算机视觉中的复杂任务。人类面部结构精细，在形状、纹理和外观上存在显著差异。在医学领域，偏离常态的面部结构（如受面瘫影响的面部）尤为需要精确分析以深入研究。眨眼过程中包含的细微运动是重要研究领域之一，其机制尚未完全明晰，需要高分辨率、高时间特异性的分析才能实现细致认知。然而，当前多数先进的计算机视觉技术需要编程技能，这对缺乏此类技能的医学工作者而言存在显著的使用障碍。为此，我们开发了耶拿面瘫工具箱（JeFaPaTo）。该工具箱采用前沿计算机视觉算法，并为非编程背景用户提供友好交互界面，旨在降低高级面部分析工具在医学领域的应用门槛，简化其融入临床工作流的流程。眼睑闭合状态对医学专家具有重要临床意义，例如在面瘫或帕金森病等疾病的评估中。由于面神经损伤可能导致眼睑闭合功能受损，进而引发多种不良并发症，因此仅简单区分睁眼与闭眼状态无法满足精细化分析需求。真正具有临床价值的关键参数包括：闭合持续时间、双侧同步性、闭合速度、完全闭合程度、两次眨眼间隔时间以及单位时间内的眨眼频率。这类精细化分析将有助于医学专家深入理解眨眼机制、识别异常模式并探索更优的眼部治疗方案。