【作者】 李亮；

【导师】 叶德谦；

【作者基本信息】 燕山大学 ， 计算机应用技术， 2009， 硕士

【摘要】 指纹识别技术是一种应用最广泛的生物识别技术。它的核心是指纹匹配算法。人们在长期的研究过程中,提出了多种指纹匹配算法。近几年,隐马尔可夫模型被应用于指纹匹配研究。但现有的基于隐马尔可夫模型的指纹匹配算法仍存在着许多不足。通过收集和分析大量有关指纹匹配技术的材料,本文对现有的基于隐马尔可夫模型的匹配算法中的几个关键环节做了深入的研究,并提出了相应的改进方法。主要研究工作如下:首先,在指纹特征选择方面,本文针对现有算法对指纹特征数据利用不充分的缺陷,提出了一种基于指纹方向、曲率、频率信息的特征向量构造方法。该方法可以明显地提高特征向量对指纹纹理变化的描述能力。为进一步提高整个匹配算法的识别正确率奠定了基础。其次,在指纹特征观测序列集合构造部分,本文提出了一种基于模板的核心点定位方法。该算法将定位过程分为两个阶段,第一阶段在方向图的基础上结合核心点周围特殊的结构特征,完成粗定位,确定备选点。第二阶段对备选点进行识别模板判断,甄别出真正的参考点。在这之后,本文又通过研究指纹特征信息的分布情况,调整了特征窗口的位置,使窗口中可以包含更多的特征信息。为进一步提高匹配算法的正确率做好了准备。再次,在模型构造部分,本文从指纹匹配技术的特点出发,简化了原有匹配模型的结构,提出了一种新的识别模型。该模型由5个一维隐马尔可夫模型组成,其具有更强的特征描述能力。最后,采用Visual C++和Matlab7.0实现了论文中提出的算法,并利用FVC2004指纹库对论文中的算法进行验证。实验结果表明本文提出的匹配算法具有较高的匹配正确率。更多还原

【Abstract】 Fingerprint identification is one of the most widely used biometric technology. As the core of fingerprint identification, many different fingerprint matching algrithms have been proposed. The algorithm based on Hidden Markov Model has attracted the most attention in recent years, but there are still some deficiencies waiting for solution. For overcoming some deficiencies in existing algorithm, a thorough study on several key points of the existing algorithm has been done and several improvements have been provided in this paper. The detail is as follow.Firstly, as for fingerprint feature selection, the existing algorithm doesn’t make the best of the fingerprint feature information. For overcoming this defect, a new feature vector construction method was provided in this paper. The new vector was composed of orientation information, frequency information and curvature information. Since the new vector contained more information, it had more powerful description ability on fingerprint texture changes and laid a foundation for improving the recognition correct rate.Secondly, a new core point alignment algorithm based on identification mask was provided in this paper. The whole process was divided into two stages. The first stage was to find the alternative points. The second stage was to make sure the real core point using the identification mask. In addition, the position of the feature window was adjusted grounding on the distribution of feature information. As the result, there were more feature information in the feature window. It was helpful to enhance the accuracy of the matching algorithm.Thirdly, a new model was provided at the part of model construction, which was composed of five one-dimensional hidden Markova models. This model had more powerful ability on feature description than the existing one. Finally, the provided algorithm was realized with Visual C++ and Matlab 7.0. And the algorithm was verified with the FVC2004 fingerprint data base. Tests and analyzing showed that the provided algorithm had higher recognition correct rate than the existing one.更多还原