Abstract: A feature extraction method using Curvelet transform is presented for face recognition.Contrasting with wavelet transform,Curvelet transform directly takes edges as the basic representation elements and is anisotropic with strong direction.It is a multiresolution,band pass and directional function analysis method and is useful to represent the image edges and the curved singularities in images more efficiently.It yields a more sparse representation of the image than wavelet and ridgelet transform.In face recognition,the Curvelet coefficients can better represent the main features of the faces.The simulation shows that the proposed method is better than wavelet based method.

Key words: Curvelet transform, face recognition, Support Vector Machine（SVM）, wavelet transform

摘要： 人脸的主要特征是曲线信息,提出了一种基于Curvelet变换的人脸识别算法。Curvelet变换在表达图像的曲线奇异性时,比小波变换和脊波变换能获得更稀疏的图像表示。在人脸识别中,用人脸的曲波系数来提取特征能更好地反映人脸的主要特征,文中使用支持向量机进行了识别。结果表明该方法比小波方法更有效。

关键词: 曲波变换, 人脸识别, 支持向量机, 小波变换