基于协同训练的指纹图像分割算法

Fingerprint segmentation algorithm based on co-training

指纹图像分割是自动指纹识别系统预处理中最关键的技术之一.精确、可靠地将指纹图像从背景中分割出来,能够加快后续工作的处理速度,提高识别算法的准确性.传统的分割算法需要大量已标记的指纹图像作为训练数据,但实际应用中获取标记样本比较繁琐和耗时.为综合利用已标记和未标记的指纹图像,提出一种基于协同训练的半监督指纹图像分割算法:CoSeg.该算法在基于像素水平的Coherence、Mean、Variace(CMV)特征体系下,使用标记盒和支持向量机作为基分类器进行协同训练.在FVC2002指纹库上的实验结果表明,CoSeg能够在标记信息较少的情况下取得较好的性能,并在处理低质量指纹图像时表现出较强的鲁棒性.

Fingerprint segmentation is one of the key preprocessing steps in an automated fingerprint identification system (AFIS). Effective segmentation of the fingerprint from the background could speed up following processes and improve recognition accuracy. However,in traditional segmentation algorithms,it is essential to obtain lots of labeled fingerprints,which are usually more expensive than unlabeled ones. To incorporate labeled and unlabeled data together,this paper proposed CoSeg,a semi-supervised fingerpri...