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首页> 外文会议>18th International Symposium on Computer and Information Sciences - ISCIS 2003; Nov 3-5, 2003; Antalya, Turkey >Facial Expression Recognition Based upon Gabor-Wavelets Based Enhanced Fisher Model
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Facial Expression Recognition Based upon Gabor-Wavelets Based Enhanced Fisher Model

机译：基于Gabor-小波的改进Fisher模型的面部表情识别

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摘要

This work deals with how the machine classifies human facial expressions, which has been a challenging problem for many researchers from the diverse areas. The facial expression recognition system mainly consists of two cascade stages: the representation method for the facial images at the front and the facial emotion classifier at the back. The Gabor-wavelets based method has shown promising performance because of its efficient representation and biological implication. Here we focus on the classification method to obtain high recognition rate of facial expressions. Results suggest that enhanced Fisher discrimination model, which had been used for the face recognition task, outperformed Principal Component Analysis (PCA) based classifier (or the neural network) with the 93% correction rate, when it is combined with the Gabor representation.
机译：这项工作涉及机器如何对人的面部表情进行分类，这对来自不同领域的许多研究人员来说是一个具有挑战性的问题。面部表情识别系统主要由两个级联阶段组成：正面的面部图像表示方法和背面的面部情感分类器。基于Gabor小波的方法由于其有效的表示和生物学意义而显示出令人鼓舞的性能。在这里，我们专注于分类方法以获得较高的面部表情识别率。结果表明，与Gabor表示相结合时，用于人脸识别任务的增强型Fisher鉴别模型优于基于主成分分析（PCA）的分类器（或神经网络），校正率达93％。

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