基于Curvelet的边缘纹理特征提取及表情识别

Edge texture feature extraction and expression recognition based on Curvelet

针对小波变换在提取图像边缘特征上的局限性,提出一种使用Curvelet变换进行边缘纹理特征提取的表情识别方法。Curvelet变换在表达图像的边缘曲线上的奇异性时比小波变换更能得到稀疏的图像表示。在表情识别中,对表情图像使用Curvelet变换得到Curvelet系数作为边缘纹理特征能更好地反映表情的变化,使用K最邻近结点算法进行了识别。结果表明在表情识别中该方法比小波变换更有效。

For the wavelet transform has limitations to extract features of the edge of the images, a method of the facial expres- sion recognition is proposed that using curvelet transform to extract features of the edge of the images. The curvelet transform can get more representation of sparse images than the wavelet transform on the representation of the singular of the edges of the image curve. The curvelet coefficient that can be got by using the curvelet transform on the facial images as the edge of the tex- ture features can better reflect the changes in the facial expression, and the k-nearest neighbor algorithm is used to recognition different expression in this paper. The result shows that the method proposed in this paper is more effective than the wavelet transform in the expression recognition.