Automatic Fingerprint Identification Using Cluster Algorithm
Ren Qun, Tian Jie, He Yuliang and Cheng Jiangang AILAB, Institute ofAutomation, the Chinese Academy o Sciences, Beijing, 100080 f Email: {qun.ren.jie.tian, yuliang. he, jiangang.cheng,’ @maiLia.ac.cn
Abstract
In this paper. a newfingerprint identifiration technique is presented, which mainly consists of three modules: enrolment module, identification module and feedback module. In the identification module. clustering algorithm is used to detect similar minutiae group from multiple template images generaredfrom the same finger and create the cluster core set. An algorithm compares the simiariiy level bemeen the minutiae oftestfingerprint and the cluster core set and returns a likely list ofcandidates. In feedback module. we propose a path to learn and train the cluster core vector based on the assessment of cluster solution. The experiment results demonstrate that this similariiy-searching approach proves suitable for one-tomany matching offingerprints on large-scale databases. With the feedback module the proposedfingerprint identification scheme has inspiring identi$cation performance of application.
1. Introduction
Fingerprint has been used as a good means of personal identification for a long time due to the characteristic of uniqueness and immutability. Fingerprint recognition is a rapidly evolving technology, which is being widely used in forensics and has the potential to be used in a large range of civilian application areas such as access control, financial security, information security, etc. Since manual fingerprint verification is extremely tedious and timeconsuming, automatic fingerprint identification systems (AFIS) are in great demand. Identification problem, a particular case of “point pattern matching”, necessitates a large database search of individuals to determine whether a person is already in the database. Hence, the challenge of fingerprint identification application is...