目的 指纹匹配是自动指纹识别系统研究的核心内容之一，匹配算法的好坏直接影响识别系统的效能。目前，大多数点模式匹配算法都依赖于指纹方向场的求取，由于输入的指纹图像存在平移、旋转和尺度变化，因此同一个手指在不同时间获得的指纹图像的方向场是不同的，这不仅增加了计算量，也影响了指纹识别的精度。针对上述问题，提出了无方向的三角形匹配算法。方法 提出的三角形匹配算法是以平面中任意点与一个确定的三角形之间的位置结构稳定性为理论基础的。首先，分别在待识指纹图像和模板指纹图像中确定基准三角形；其次，将各个特征点与基准三角形三个顶点的距离组成有序三数组；最后，利用数组的相等程度对指纹相似度进行匹配判断。结果 采用国际标准测试库FVC2004进行综合性能比对实验，实验结果表明，与其他几种匹配算法相比，本文方法在识别精度上提高了27.97%~33.81%，在比对时间上降低了3%~5%，在不同旋转角度下误匹配率平均降低了约86.63%，对噪声、平移、旋转和形变有足够的适应能力，具有较高的容错能力和鲁棒性。结论 无方向的三角形匹配算法是一种全局模式的算法，该算法不受指纹图像方向及其位置的影响，实现过程简单，识别精度高，平均比对时间少，适用于处理不同类型的图像数据。

Objective Fingerprint identification is an important and efficient technique used for biometric recognition.Fingerprints have become the most widely used biometric feature in recent years given their uniqueness and immutability.Fingerprint matching is a core research content of automatic fingerprint recognition systems.Matching algorithms directly influence the functions of a recognition system.Most point pattern-matching algorithms depend on the orientation field or directed graph of fingerprint images.That is,the matching of points is transformed into the matching of vectors,which are composed of two feature points.The fingerprint orientation field or the directed graph from the same finger frequently varies at different collection times because the input fingerprint images exhibit translation,rotation,and scale change.Consequently,the calculation of most point pattern-matching algorithms is extremely difficult.Point pattern-matching algorithms are also sensitive to the translation,rotation,and scale change of fingerprint images,particularly rotation.Certain parts of point pattern-matching algorithms cannot deal with fingerprint images with rotation.Therefore,a triangle-matching algorithm that is irrelevant to orientation is proposed and a detailed presentation of composing the congruent triangle is introduced in this study to improve the precision of calculation.Method A triangle exhibits stability,invariance,and uniqueness.The position structure is stable for any point and a certain triangle on a plane.The proposed triangle-matching algorithm is designed based on this theory.This algorithm efficiently avoids the orientation field or directed graph and significantly reduces calculation.The proposed algorithm,which is independent of orientation field or directed graph,also has preferable stability and robustness performance at different rotation angles.Fingerprint identification can be generally divided into three main periods:preprocessing of fingerprint images,feature extraction,and feature matching.On the basis of this framework,the proposed algorithm mainly contains three periods as follows.First,two benchmark triangles are constituted in identifying a fingerprint and a template fingerprint system.Second,the ordered arrays are composed of the distances from every feature point to three vertices of a benchmark triangle.Third,fingerprint image matching is decided based on the similarity degree of ordered arrays.Result The overall performance comparison experiments,such as complete fingerprint-matching process,equal error rate,false match rate,false acceptance rate,receiver operating curve,and match time,are completed using the FVC2004 fingerprint database,which is an international standard test library.Experimental results show that compared with other fingerprint-matching algorithms,the proposed algorithm successfully improves accuracy by 27.97% to 33.81%,reduces matching time by 3% to 5%,and decreases the average error in matching by approximately 86.63%.The proposed algorithm also outperforms the compared algorithms in terms of adaptive capacity,accuracy,and robustness for fingerprint images with noise,translation,rotation,and deformation.Conclusion The proposed algorithm is a global model-matching algorithm,which is unconstrained by the fingerprint orientation field and the locations of fingerprint images.Calculation is significantly reduced compared with other point pattern fingerprint-matching algorithms.The process and implementation of the proposed algorithm are simply based on elementary mathematics.The experimental results indicate that the proposed algorithm demonstrates preferable adaptive performance for fingerprint images with noise,translation,rotation,and deformation.Furthermore,the proposed algorithm exhibits good robustness and can handle different types of images.