面向虹膜识别的Gabor特征筛选研究

针对2D-Gabor虹膜特征并不稳定，影响虹膜识别率的问题，提出了一种从多尺度、多方向2D-Gabor小波提取的虹膜特征中，筛选稳定特征应用于虹膜识别的方法。对虹膜图像采用多通道Gabor小波提取虹膜图像特征，然后通过自定义筛选准则从多维特征中筛选出最优特征参数并编码，用Hamming距进行特征匹配识别。基于CASIA虹膜图像库进行实验，结果表明该方法扩大了类内匹配与类间匹配之间的Hamming距，降低了等错率，同时降低了编码的长度，加快了特征匹配速度。

Study of Gabor feature selection for iris recognition

In common,the instability of 2D-Gabor iris features decreases the recognition ratio of the iris.To settle the problem,this paper proposes an approach to select the secure features for iris recognition from multiple-scale 2D-Gabor features.The features of iris are extracted by multi-channel Gabor filtering on the iris images.Then the optimal feature parameters are selected by the screening rules defined and coded.And the feature matching is performed by using Hamming distance.Experimental results on the CASIA iris database images show that this approach enlarges Hamming distance between inter-class and intra-class,and at the same time the equal error rate(EER)is decreased.Besides,the length of code is reduced and speed of feature matching is improved.