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首页> 外文期刊>Journal of Neuroscience Methods >Automated Facial Action Coding System for dynamic analysis of facial expressions in neuropsychiatric disorders.
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Automated Facial Action Coding System for dynamic analysis of facial expressions in neuropsychiatric disorders.

机译：自动面部动作编码系统，用于动态分析神经精神疾病中的面部表情。

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Facial expression is widely used to evaluate emotional impairment in neuropsychiatric disorders. Ekman and Friesen's Facial Action Coding System (FACS) encodes movements of individual facial muscles from distinct momentary changes in facial appearance. Unlike facial expression ratings based on categorization of expressions into prototypical emotions (happiness, sadness, anger, fear, disgust, etc.), FACS can encode ambiguous and subtle expressions, and therefore is potentially more suitable for analyzing the small differences in facial affect. However, FACS rating requires extensive training, and is time consuming and subjective thus prone to bias. To overcome these limitations, we developed an automated FACS based on advanced computer science technology. The system automatically tracks faces in a video, extracts geometric and texture features, and produces temporal profiles of each facial muscle movement. These profiles are quantified to compute frequencies of single and combined Action Units (AUs) in videos, and they can facilitate a statistical study of large populations in disorders known to impact facial expression. We derived quantitative measures of flat and inappropriate facial affect automatically from temporal AU profiles. Applicability of the automated FACS was illustrated in a pilot study, by applying it to data of videos from eight schizophrenia patients and controls. We created temporal AU profiles that provided rich information on the dynamics of facial muscle movements for each subject. The quantitative measures of flatness and inappropriateness showed clear differences between patients and the controls, highlighting their potential in automatic and objective quantification of symptom severity.
机译：面部表情被广泛用于评估神经精神疾病中的情绪障碍。 Ekman和Friesen的面部动作编码系统（FACS）对来自面部外观的明显瞬时变化的各个面部肌肉的运动进行编码。与基于表情归类为典型情感（幸福，悲伤，愤怒，恐惧，厌恶等）的面部表情评级不同，FACS可以编码模棱两可和微妙的表情，因此可能更适合分析面部表情的细微差别。但是，FACS评级需要大量培训，并且既耗时又主观，因此容易产生偏差。为了克服这些限制，我们基于先进的计算机科学技术开发了自动FACS。该系统自动跟踪视频中的面部，提取几何和纹理特征，并生成每个面部肌肉运动的时间轮廓。对这些配置文件进行量化以计算视频中单个动作单元和组合动作单元（AU）的频率，它们可以促进对已知会影响面部表情的各种疾病的大量人群进行统计研究。我们从时间性AU轮廓自动得出定量测量扁平和不适当面部影响的方法。通过在一项初步研究中说明了自动FACS的适用性，将其应用于来自八位精神分裂症患者和对照的视频数据。我们创建了时间AU配置文件，为每个对象提供了丰富的面部肌肉运动动态信息。扁平度和不适度的定量测量显示患者与对照组之间存在明显差异，突出了他们在自动和客观量化症状严重性方面的潜力。

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