基于多特征判别分析的指纹图像分割方法

Segmentation of Fingerprint Images Using Discriminant Analysis of Multiple Features

针对指纹图像的分割问题,提出了一种基于多特征判别分析的自适应分割算法。对于给定的待分割图像,该算法从每个特征在该图像上整体的分布出发,构造出综合考虑各个特征的两类分类能力的分类器,然后利用该分类器对图像中的每个子块做出前景或背景的判断。与已有的基于分类器的分割方法相比,该方法无需从数据库中人工采集样本用以训练分类器,实现了图像级别的自适应分割。算法在FVC2004竞赛的公开数据库上进行了测试,实验结果证明了该分割算法的有效性。

A novel algorithm using discriminant analysis of multiple features （DAMF） is presented for adaptive fingerprint image segmentation. Three block-level features including the variance, the orientation coherence measure and the total variation are used by the proposed method. For a given fingerprint, probability distribution of each feature over the whole image is estimated, and then a classifier adaptive to the fingerprint is constructed by considering the discriminability of each feature for two-class （background and foreground） classification problem. The fingerprint image is segmented by classifying each image block via the classifier. Mathematical morphology and region boundary smoothing is applied as postprocessing to obtain compact clusters and to reduce the classification error. The performance of new algorithm is evaluated on FVC2004 database, and the experimental results show that new algorithm can segment the fingerprints effectively, and also improve the accuracy of matching substantially.