Pain Intensity Evaluation through Facial Action Units

Zuhair Zafar, N. Khan
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引用次数: 33

Abstract

In this work we present a system that enables automatic estimation of Pain from image sequences with frontal views of faces. The system uses facial characteristic points to characterize different Action Units (AU) of pain and is able to operate in cluttered and dynamic scenes. Geometric features are computed using 22 facial characteristic points. We use k-NN classifier for classifying AU. Only action units relevant to pain are classified. Validation studies are done on UNBC McMaster Shoulder Pain Archive Database [8]. We also classify action unit intensities for evaluating pain intensity on a 16 point scale. Our system is simpler in design compared to the already reported works in literature. Our system reports AU intensities on a standard scale and also reports pain intensity to assess pain. We have achieved more than 84% accuracy for AU intensity levels and 87.4% area under ROC curve for pain assessment as compared to 84% of state-of-the-art scheme.
通过面部动作单元评估疼痛强度
在这项工作中,我们提出了一种系统,可以自动估计面部正面视图图像序列的疼痛。该系统使用面部特征点来表征疼痛的不同动作单元(AU),并能够在混乱和动态的场景中运行。利用22个面部特征点计算几何特征。我们使用k-NN分类器对AU进行分类。只有与疼痛相关的动作单位才被分类。验证研究是在UNBC McMaster肩痛档案数据库[8]上完成的。我们还分类行动单位强度评估疼痛强度在16分量表。与已有文献报道的作品相比,我们的系统在设计上更简单。我们的系统以标准尺度报告AU强度,也报告疼痛强度以评估疼痛。我们在AU强度水平上的准确率超过84%,在疼痛评估的ROC曲线下的面积达到87.4%,而最先进的方案为84%。
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