Curvelet变换用于人脸特征提取与识别

Face Feature Extraction and Face Recognition Using Curvelet Transform

针对小波变换用于人脸识别时难以充分描述人脸曲线特征的问题,提出用Curvelet变换进行人脸特征提取与识别的新方法。将人脸图像进行Curvelet变换,提取进一步压缩的低频系数和高频各子带的Curvelet能量特征为人脸特征向量,并采用支持向量机进行特征分类与识别。以Orl和Yale人脸库进行测试,结果表明,该方法相比小波变换法识别效果更佳,且对光照、姿态和表情变化具有良好的鲁棒性。

As the wavelet transform cannot well express curve characteristics of face image, we propose a feature extraction and face recognition method based on curvelet transform. The face image is first decomposed with curvelets.Low frequency coefficients are compressed to reduce dimension of feature vectors, and curvelet energy features are calculated in each high frequency subband. The reduced vectors are used to represent features of the face image.Features are then classified using support vector machine. Experimental results on Orl and Yale face databases show that the proposed method is superior to wavelet methods. It is robust to varying illumination conditions, face poses and expressions.