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Local Sensitive Frontier Analysis based facial expression recognition

机译:基于局部敏感前沿分析的面部表情识别

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摘要

Facial expression recognition plays an important role in interactive entertainment. In this paper, LSFA (Local Sensitive Frontier Analysis) a novel feature extraction method is introduced for facial expression recognition. LSFA is designed as manifold based feature extraction method to obtain useful features from the facial expression pictures, since the facial expression scatter in high dimensional space as a point will embed in low dimensional manifold. From comparing several feature extraction methods in the experiment, it can be found that this algorithm gets better expression recognition result.
机译:面部表情识别在互动娱乐中起着重要作用。在本文中,引入了一种新的特征提取方法——LSFA(局部敏感前沿分析),用于面部表情识别。 LSFA被设计为基于流形的特征提取方法,以便从面部表情图片中获取有用的特征,因为面部表情在高维空间中的散布是因为一个点将嵌入在低维流形中。通过比较实验中几种特征提取方法,可以发现该算法获得了较好的表情识别结果。

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