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. 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. 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. 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.