应用复小波和独立成分分析的人脸识别

结合双树复小波变换（DT-CWT）和独立成分分析（ICA）提出了一种人脸识别新方法。该方法首先应用双树复小波变换提取图像的特征向量，接着通过主成分分析（PCA）降低特征向量的维数，在此基础上应用独立成分分析提取统计上独立的特征向量，然后基于相关系数的分类器对特征向量进行分类。双树复小波变换具有方向与尺度选择性，并能有效的保持图像的频域信息，其与独立成分分析相结合提取的特征具有良好的分类性能。在ORL和AR人脸图像数据库上进行算法验证的结果表明该方法的有效性。

Face recognition using complex wavelet and independent component analysis

A novel face recognition method is proposed by adopting the dual-tree complex wavelet transform (DT-CWT) and independent component analysis (ICA). The DT-CWT is applied to face images to extract the feature vectors. The dimension of the salient feature vectors is reduced by principal component analysis (PCA). ICA further reduces the feature redundancies and derives independent feature vectors for the correlation-based classifier. DT-CWT has the capability of selectivity on scale and orientation, and preserves more information in frequency domain. Features extracted by DT-CWT and ICA can obtain the excellent performance on classification. Extensive experimental results demonstrated the validity of the proposed method using the ORL and AR database.