Analysis and Modeling of Faces and Gestures
Analysis and Modeling of Faces and Gestures
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面部和手势的分析和建模
DOI：
10.1007/978-3-540-75690-3_6
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Moore S
中科院分区：
文献类型：
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作者：
Moore S
在过去的二十年中，自动面部表情识别已经成为一个活跃的研究领域。面部表情是非语言交流的重要渠道，可以提供情感和意图的线索。本文介绍了一种新的人脸表情识别方法，通过组装轮廓片段作为判别分类器，并提高他们形成一个强大的准确分类器。检测是快速的，因为使用对倒角图像的有效查找来评估特征，该倒角图像对特征的响应进行加权。提出了一种基于分类器响应的投票方案，并给出了一种基于投票方案的Ensemble分类技术。这项研究的结果是一个6类分类器（愤怒，喜悦，悲伤，惊讶，厌恶和恐惧的6种基本表情），其中一些表情的竞争结果达到了96%。由于分类器的计算速度非常快，因此该方法的操作速度远高于帧速率。我们还演示了如何构建一个专用的分类器，以提供最佳的自动参数选择的检测器，允许真实的时间操作无约束的视频。
Over the last two decades automatic facial expression recognition has become an active research area. Facial expressions are an important channel of non-verbal communication, and can provide cues to emotions and intentions. This paper introduces a novel method for facial expression recognition, by assembling contour fragments as discriminatory classifiers and boosting them to form a strong accurate classifier. Detection is fast as features are evaluated using an efficient lookup to a chamfer image, which weights the response of the feature. An Ensemble classification technique is presented using a voting scheme based on classifiers responses. The results of this research are a 6-class classifier (6 basic expressions of anger, joy, sadness, surprise, disgust and fear) which demonstrate competitive results achieving rates as high as 96% for some expressions. As classifiers are extremely fast to compute the approach operates at well above frame rate. We also demonstrate how a dedicated classifier can be consrtucted to give optimal automatic parameter selection of the detector, allowing real time operation on unconstrained video.