Latent fingerprint identification is one of the leading forensic activities to clarify criminal acts. However, its computational cost hinders the rapid decision making in the identification of an individual when large databases are involved. To reduce the search time used to generate the fingerprint candidates’ order to be compared, fingerprint indexing algorithms that reduce the search space while minimizing the increase in the error rate (compared to the identification) are developed. In the present research, we propose an algorithm for indexing latent fingerprints based on minutia cylinder codes (MCC). This type of minutiae descriptor presents a fixed structure, which brings advantages in terms of efficiency. Besides, in recent studies, this descriptor has shown an identification error rate, at the local level, lower than the other descriptors reported in the literature. Our indexing proposal requires an initial step to construct the indices, in which it uses k-means++ clustering algorithm to create groups of similar minutia cylinder codes corresponding to the impressions of a set of databases. K-means++ allows for a better outcome over other clustering algorithms because of the selection of the proper centroids. The buckets associated with each index are populated with the background databases. Then, given a latent fingerprint, the algorithm extracts the minutia cylinder codes associated with the clusters’ indices with the lowest distance respect to each descriptor of this latent fingerprint. Finally, it integrates the votes represented by the fingerprints obtained to select the candidate impressions. We conduct a set of experiments in which our proposal outperforms current rival algorithms in presence of different databases and descriptors. Also, the primary experiment reduces the search space by four orders of magnitude when the background database contains more than one million impressions.

中文翻译：

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一种基于细节柱码聚类的快速潜指纹识别索引算法


潜指纹识别是澄清犯罪行为的主要法医活动之一。然而，当涉及大型数据库时，其计算成本阻碍了个体识别的快速决策。为了减少用于生成要比较的指纹候选顺序的搜索时间，开发了指纹索引算法，该算法可以减少搜索空间，同时最大限度地减少错误率的增加（与识别相比）。在本研究中，我们提出了一种基于细节柱面码（MCC）的潜在指纹索引算法。这种类型的细节描述符呈现出固定的结构，这在效率方面带来了优势。此外，在最近的研究中，该描述符在局部水平上显示出识别错误率低于文献中报道的其他描述符。我们的索引建议需要一个初始步骤来构建索引，其中它使用 k-means++ 聚类算法来创建与一组数据库的印象相对应的相似细节圆柱体代码组。由于选择了正确的质心，K-means++ 比其他聚类算法能获得更好的结果。与每个索引关联的存储桶都填充有后台数据库。然后，给定潜在指纹，该算法提取与该潜在指纹的每个描述符具有最小距离的簇索引相关联的细节柱面代码。最后，整合获得的指纹所代表的选票来选择候选印象。我们进行了一系列实验，在这些实验中，我们的建议在存在不同数据库和描述符的情况下优于当前的竞争对手算法。 此外，当后台数据库包含超过一百万次展示时，主要实验将搜索空间减少了四个数量级。