针对小波变换不能充分描述人脸曲线特征的缺点，本文提出一种基于曲波域与核主成分分析 (KPCA)的人脸识别算法。采用多尺度、多方向的曲波 (Curvelet)变换提取图像特征，不仅具有更高的逼近精度和更好的稀疏表达能力，而且其变换系数能有效表示沿曲线的奇异性。进一步使用核主成分分析 (KPCA)将曲波特征系数投影到更具表达力的核空间中，通过最近邻分类器进行分类。并在 JAFFE人脸库中、 ORL人脸库以及 FERET人脸库中做了多组实验，实验结果表明该方法在图像降维和识别率方面都达到了较好的效果。

Since wavelet transform can not fully describe facial curves features, a face recognition algorithm based on curvelet domain and Kernel Principal Component Analysis (KPCA) is proposed. Using multi-scale, multi-directional curvelet transform to extract image features not only has higher approximation accuracy and better sparse expression, but also can effectively express the singularity along the curve. Then, KPCA is used to project curvelet feature coefficient into the more expressive kernel space. Finally, the nearest method is adopted for classification. The results indicate this algorithm has better effect on image dimension reduction and face recognition rate in the JAFFE face database, ORL face database and FERET face database.