指纹库容量对指纹检索结果的影响

The Impact of Database Size on Fingerprint Database Searches

近年来,指纹库容量的快速增长导致指纹比对难度加大,尤其体现在检索结果中相似异源指纹对比中同源指纹的干扰上。为了探究不同指纹库容量对同源指纹与相似异源指纹出现情况的影响,本实验建立600万人级、1000万人级和1亿人级数据库,对箕型纹三角区域各部分发起查询并讨论检索结果。结果显示:指纹库容量增长幅度越大,同源指纹出现率降低越明显,同时其排位显著降低,而相似异源指纹数量显著增加,当相似异源指纹排在同源指纹之前时,会对指纹鉴定人员造成干扰。此外,还发现特征点密度可以影响相似异源指纹的出现数量,特征点密度越大,特征组合的特定性越小,越容易产生相似异源指纹。本研究旨在增强大数据条件下指纹鉴定人员的风险意识。

In recent years,the rapid growth in the capacity of AFIS(Automatic Fingerprint Identification System)databases has led to an increasing difficulty infingerprint identification,particularly in the interference of Close Non-Matches(CNMs)with homologousfingerprints in the search results.Before using AIfingerprint recognition algorithms,CNMs with higher scores and higher rankings may appear in the candidate list.In order to explore the influence of different AFIS database sizes on the occurrence of homologousfingerprints and CNMs under the condition of traditional comparison algorithm,this experiment established 6-million-people,10-million-people,and 100-million-people level databases by setting thefingerprint card imprinting time during querying,then initiated querying and discussed the search results of each part of the delta area of the loop(root part,center part,and periphery part),and the annotations of each part were the 10 minutiae closest to the apex of the bottom-type line.The results show that when the capacity of thefingerprint database grows,the occurrence rate of homologousfingerprints decreases,and their ranking decreases at the same time,and the larger the size of the growth of thefingerprint database capacity,the more obvious the degree of decrease.When the capacity of thefingerprint database grows,the number of occurrences of CNMs increases,and the number of corresponding points of CNMs also increases,and the larger the scale of the growth of thefingerprint database,the more obvious the degree of increase.In this experiment,three high-level CNMs with 10 corresponding points were found in the 10-million-people and 100-million-people level databases.When CNMs are ranked before homologousfingerprints,it may cause interference tofingerprint examiners.In addition,it was also found that the number of occurrences of CNMs in the three parts of the delta area of the loop in different databases showed that the root part>the center part>the periphery part,which was related to the density of the minutiae in the three parts.The higher the density of the minutiae,the smaller the distance between the minutiae,and the smaller the area of distribution of the unit number of the minutiae,the higher the probability of the repetition of the same distribution pattern,and the lower the specificity of the minutiae configurations,the easier it is to produce feature similarity.This study aims to improve the risk awareness offingerprint examiners under big data conditions.In addition to being cautious,the industry may need to do a lot of work from upgradingfingerprint matching algorithms and establishing newfingerprint identification paradigms.