一种基于点模式匹配的指纹识别方法

把指纹用于身份认证已有上丰年的历史，自动指纹识别（AFIS）已在各个领域广泛应用，除了传统的司法公安领域，自动指纹识别还可以作为计算机操作系统和网络访问认证用户的手段，以及用于金融保险方面的身份确认，指纹匹配是AFIS的最重要问题之一。一般用脊线的分歧点和端点这样的细节点来表示一个指纹，并通过细节匹配来进行指纹匹配，这样指纹匹配就转化为点模式匹配的问题，传统的点模式匹配算法存在运算量过大且难于解决所有的非弹性形变等问题。文章提出了一种基于聚类的点模式匹配方法，来进行指纹识别，有效地解决了如何对数量和位置都不尽相同的两幅指纹细节图象进行最优匹配的问题，设计并给出了利用矢量对匹配确定图象间校准函数的流程，并通过实验证明所提出的算法具有较快的速度和较高的准确率。

Fingerprint Identification Using Point Pattern Matching

Fingerprint is those lines distributing on the finger surface. It's unique and stable. It has been hundreds years to use fingerprint to identity. AFIS (Auto Fingerprint Identification System) has been widely used. Besides traditional criminal, AFIS has been used to validate user in OS and web access, and embedded AFIS also has been used in ATM, credit card and door-lock. Fingerprint matching is one of the most important problems in AFIS. In general, the minutiae such as ridge endings and ridge bifurcation is to be used to represent a fingerprint and the fingerprint matching through minutiae matching. Based on this observation and by representing the minutiae as a point pattern, an automatic fingerprint verification problem may be reduced to a point pattern matching (minutia matching) problem. Point pattern matching is a famous problem in the field of pattern recognition. For a good point pattern matching approach, it is able to perform the geometrical invariant quantities (translation, rotation, and scaling) efficiently. Various algorithms have been proposed for point pattern matching. For example, the relaxation approach handles translational difference only and the complexity of triangles approach is very high. In this paper, an algorithm for fingerprint identification using point pattern matching based on cluster approach is proposed, which effectively solves the problems of optimal matching between two fingerprint minutiae images under geometrical transformation and minutiae quantity change. Process which bases on the matching of vector pairs is developed to determine the registration parameters. The experimental results show that the proposed matching algorithm is fast and has high accuracy.