Department of Forensic Science, Zhejiang Police College, Hangzhou, China.
School of Forensic Science, People's Public Security University of China, Beijing, China.
J Forensic Sci. 2021 Jul;66(4):1482-1494. doi: 10.1111/1556-4029.14698. Epub 2021 Feb 26.
Fingerprint identification errors may be due to the high similarity of fingerprints from different sources, especially when queries are conducted in a large database with the application of the Automatic Fingerprint Identification System (AFIS). In this study, a database of ten-prints of 6.964 million individuals was used; 20 sets of 60 simulated fingermarks of different qualities were used and compared with fingerprints from the database. A total of 245 queries were conducted based on both the quality of each fingermark and the number of minutiae. Four types of results were obtained from these queries on the large database, and were categorized as follows: (A) Neither Same Source nor Close Non-Match appears in the candidate list, (B) Only Same Source appears, (C) Only Close Non-Matches appear, and (D) Both Same Source and Close Non-Matches appear. When the quality of the fingermark was improved, more minutiae could be identified, and the degree of accuracy of the placement as well as orientation was higher. As a result, highly Close Non-Match fingerprints appeared; this made it harder to distinguish these fingerprints from Same Source fingerprints, especially in the large database. We concluded that more highly Close Non-Matches might appear when the database is consistently expanded, and an increasing number of Close Non-Matches might be found with a higher ranking and score than the Same Source; this would make the identification harder for examiners and might increase the possibility of identification errors.
指纹识别错误可能是由于来自不同来源的指纹高度相似，尤其是在使用自动指纹识别系统 (AFIS) 在大型数据库中进行查询时。在本研究中，使用了一个包含 696.4 万人十指指纹的数据库；使用了 20 组不同质量的 60 枚模拟指纹，并与数据库中的指纹进行了比较。总共根据每个指纹的质量和特征点数量进行了 245 次查询。从这些对大型数据库的查询中获得了四种类型的结果，并进行了如下分类：(A)候选列表中既没有相同来源也没有接近非匹配，(B)只有相同来源，(C)只有接近非匹配，(D)相同来源和接近非匹配都有。当指纹质量提高时，可以识别出更多的特征点，并且位置和方向的准确性更高。结果，出现了高度接近的非匹配指纹；这使得这些指纹与相同来源的指纹更难区分，尤其是在大型数据库中。我们得出的结论是，随着数据库的不断扩大，可能会出现更多的高度接近的非匹配，并且与相同来源相比，可能会有更多的接近非匹配具有更高的排名和分数；这将使鉴定变得更加困难，并且可能会增加鉴定错误的可能性。
