从疼痛的面部自动检测动作单位:比较形状和外观特征

P. Lucey, J. Cohn, S. Lucey, S. Sridharan, K. Prkachin
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引用次数: 24

摘要

最近的心理学研究表明,面部运动是衡量疼痛的可靠指标。与疼痛相关的面部运动的自动检测将有助于患者护理,但在技术上具有挑战性。面部运动可能很微妙,并伴有头部方向的突然变化。主动外观模型(AAM)已被证明对自然发生的面部行为具有鲁棒性,但基于AAM的自动检测动作单元(AUs)的努力很少。利用肩袖损伤患者的图像数据,我们描述了一个基于aam的自动系统,该系统将形状和外观解耦,以逐帧检测AUs。目前大多数AU检测方法仅使用外观特征。我们探讨了形状和外观在AU检测中的相对功效。与人类观察者的经验一致,我们发现了动作单位和面部特征类型之间的特定关系。一些AU(如AU4、12和43)的形状比外观更容易区分,而其他AU(如AU6、7和10)的模式则相反。特定于au的特性集可能产生最佳结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatically detecting action units from faces of pain: Comparing shape and appearance features
Recent psychological research suggests that facial movements are a reliable measure of pain. Automatic detection of facial movements associated with pain would contribute to patient care but is technically challenging. Facial movements may be subtle and accompanied by abrupt changes in head orientation. Active appearance models (AAM) have proven robust to naturally occurring facial behavior, yet AAM-based efforts to automatically detect action units (AUs) are few. Using image data from patients with rotator-cuff injuries, we describe an AAM-based automatic system that decouples shape and appearance to detect AUs on a frame-by-frame basis. Most current approaches to AU detection use only appearance features. We explored the relative efficacy of shape and appearance for AU detection. Consistent with the experience of human observers, we found specific relationships between action units and types of facial features. Several AU (e.g. AU4, 12, and 43) were more discriminable by shape than by appearance, whilst the opposite pattern was found for others (e.g. AU6, 7 and 10). AU-specific feature sets may yield optimal results.
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