用于虹膜识别的轮廓波特征提取

Contourlet Feature Extraction for Iris Recognition

为获得高品质的虹膜纹理特征,针对小波变换方向选择性差的局限和虹膜图像纹理丰富的特点,本文提出了一种基于轮廓波(Contourlet)变换的虹膜特征提取方法。首先对预处理后的虹膜图像进行Contourlet分解,然后根据高低频子带所表征的信息,采用不同特征提取策略,提取其低频分量的均值及标准差和不同尺度、不同方向上高频子带变换系数矩阵的能量作为特征值,最后利用支持向量机和汉明距离的方法对CASIAVer1.0和MMU两类虹膜库中的图像进行测试。实验结果表明,同基于Harr小波和离散余弦变换等特征提取方法相比,该方法可获得较好的识别性能。

In view of the limitation of poor direction selectivity about wavelet transform and iris image having rich texture features, an iris feature extraction method？ based on contourlet transform for obtaining high quality features is proposed in the paper. First of all, the preprocessed iris image is decomposed by contourlet, then, according to the information that high and low frequency subbands represent, it adopts different extraction ways, both the mean and variance of low frequency subband coefficients and the energy of high frequency subband coefficients are extracted to be the feature vectors. Finally, it carries the test on CASIA Ver1.0 and MMU iris databases with SVMs and Hamming distances. Compared with the feature extraction method？ based on the？ Harr wavelet and discrete cosine transform, the proposed method can achieve better performance.