面部表情空间建模识别

Yuwen Wu, Hong Liu, H. Zha
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引用次数: 57

摘要

本文提出了一种基于模糊积分的面部表情空间建模方法。传统的基于形状特征的表情识别方法在描述面部表情分类的不确定性以及面部特征与面部表情之间的关系方面存在问题。利用面部表情空间模型可以很容易地解决这些问题。首先,利用不同面部表情空间的模糊积分值来描述面部表情的不确定性。其次,通过在每个面部表情空间中自动构建模糊测度,处理面部特征对面部表情分类的不同影响;实验表明,该方法具有较好的描述面部表情不确定性的能力,并取得了较好的分类效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling facial expression space for recognition
In this paper, we present a method of modeling facial expression space for facial expression recognition by fuzzy integral. In traditional expression recognition methods using shape features, there are problems in describing both the uncertainty in facial expression classification and the relationship between facial features and facial expressions. Using facial expression space model, those problems can be solved easily. Firstly, we use values of fuzzy integral in different facial expression spaces to describe the uncertainty of facial expression. Secondly, by the fuzzy measure automatically constructed in each facial expression space, we deal with different effects of facial features for facial expression classification. Experiments show this method has a good ability of describing the uncertainty of facial expression and acquires good results of classification.
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