基于小波变换和独立分量分析的面部表情识别

提出了一种联合二维离散小波变换（2D-DWT）和独立分量分析（ICA）相结合的表情特征提取法。首先通过2D-DWT将当前图像分解成4个子图像，其中一子图像对应原图像的主体部分（低通部分），其余三个子图像对应图像的细节部分（高通部分）。采用ICA分别对每一子图像进行特征提取，得到的表情矢量与中性矢量的差值矢量作为特征矢量，在此基础上使用性能比较稳定的支持向量机来分析各个子带图像的识别情况。此外，还提出了一种简单有效的方法对各个子图像所提取的特征进行融合，将融合的结果作为特征矢量来识别。同其它基于静态图像识别的方法相比，所提的方法识别效果好，且具有一定泛化性和鲁棒性。

Facial expression recognition based on 2D-DWT and ICA

An efficient facial expression recognition method by combining the two-dimensional Discrete Wavelet Transform(2D-DWT) method with the Independent Component Analysias(ICA) method are proposed.First,each image is decomposed into four sub-images by using the 2D-DWT approach,and then ICA approach is used to extract features form each sub-image respectively.Then,the differences of extracted features are obtained by subtracting features of neutral expression from the features of other expressions.All the differences of features are further combined and used for facial expression classification.Moreover,considering that the discriminative features extracted from each sub-image may not share the same metric scale measure,the authors also propose an effective features combination method in this paper.These experiment results indicate that the recognition ratios of facial expression are heightened by this method.