Recognizing lower face action units for facial expression analysis
Recognizing lower face action units for facial expression analysis
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DOI：
10.1109/afgr.2000.840678
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发表时间：
2000-03
期刊：
影响因子：
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通讯作者：
Ying-li Tian;T. Kanade;J. Cohn
中科院分区：
文献类型：
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作者：
Ying-li Tian;T. Kanade;J. Cohn
大多数自动表达分析系统尝试识别一小组原型表达（例如快乐和愤怒）。然而，这种原型表达很少出现。人类的情感和意图更多地是通过一两个离散的面部特征的变化来传达的。我们开发了一种自动系统，可以根据近正面图像序列中的永久（例如嘴、眼睛和眉毛）和短暂（例如皱纹和皱纹）面部特征来分析面部表情的细微变化。提出了多状态面部成分模型来跟踪和建模不同的面部特征。基于这些多状态模型，在没有人工增强的情况下，我们检测并跟踪面部特征，包括嘴、眼睛、眉毛、脸颊及其相关的皱纹和面部皱纹。此外，我们恢复了面部特征的详细参数描述。以这些特征作为输入，神经网络算法可以识别 11 个单独的动作单元或动作单元组合。识别率达到96.7%。识别结果表明，我们的系统可以识别动作单元，无论它们是单独出现还是组合出现。
Most automatic expression analysis systems attempt to recognize a small set of prototypic expressions (e.g., happiness and anger). Such prototypic expressions, however, occur infrequently. Human emotions and intentions are communicated more often by changes in one or two discrete facial features. We develop an automatic system to analyze subtle changes in facial expressions based on both permanent (e.g., mouth, eye, and brow) and transient (e.g., furrows and wrinkles) facial features in a nearly frontal image sequence. Multi-state facial component models are proposed for tracking and modeling different facial features. Based on these multi-state models, and without artificial enhancement, we detect and track the facial features, including mouth, eyes, brow, cheeks, and their related wrinkles and facial furrows. Moreover we recover detailed parametric descriptions of the facial features. With these features as the inputs, 11 individual action units or action unit combinations are recognized by a neural network algorithm. A recognition rate of 96.7% is obtained. The recognition results indicate that our system can identify action units regardless of whether they occur singly or in combinations.