Automatic latent fingerprint identification is beneficial during forensic investigations. Usually, latent fingerprint identification algorithms are used to find a subset of similar fingerprints from those previously captured on databases, which are finally examined by latent examiners. Yet, the identification rate achieved by latent fingerprint identification algorithms is far from those obtained by latent examiners. One approach for improving identification rates is the fusion of the match scores computed with fingerprint matching algorithms using a supervised classification algorithm. This approach fuses the results provided by different lower-level algorithms to improve them. Thus, we propose a fusion of fingerprint matching algorithms using a supervised classifier. Our proposal starts with two different local matching algorithms. We substitute their global matching algorithms with another independent of the local matching, creating two lower-level algorithms for fingerprint matching. Then, we combine the output of these lower-level algorithms using a supervised classifier. Our proposal achieves higher identification rates than each lower-level algorithm and their fusion using traditional approaches for most of the rank values and reference databases. Moreover, our fusion algorithm reaches a Rank-1 identification rate of \(74.03\%\) and \(71.32\%\) matching the 258 samples in the NIST SD27 database against 29,257 and 100,000 references, the two largest reference databases employed in our experiments.

在法医调查期间，自动潜在指纹识别非常有用。通常，潜在指纹识别算法用于从先前在数据库中捕获的指纹中找到相似指纹的子集，这些指纹最终由潜在检查者进行检查。然而，通过潜在指纹识别算法获得的识别率与潜在检查者所获得的识别率相差甚远。一种提高识别率的方法是使用监督分类算法融合指纹匹配算法计​​算出的匹配分数。这种方法融合了不同的低层算法提供的结果，以改善它们。因此，我们提出了使用监督分类器的指纹匹配算法的融合。我们的建议从两种不同的本地匹配算法开始。我们用另一种与本地匹配无关的全局匹配算法替代了它们的全局匹配算法，从而创建了两个用于指纹匹配的低级算法。然后，我们使用监督分类器来组合这些较低级别算法的输出。我们的建议实现了比每个较低级别算法更高的识别率，并且对于大多数等级值和参考数据库，使用传统方法将它们融合在一起。此外，我们的融合算法达到了Rank-1的识别率 我们的建议实现了比每个较低级别算法更高的识别率，并且对于大多数等级值和参考数据库，使用传统方法将它们融合在一起。此外，我们的融合算法达到了Rank-1的识别率 我们的建议实现了比每个较低级别算法更高的识别率，并且对于大多数等级值和参考数据库，使用传统方法将它们融合在一起。此外，我们的融合算法达到了Rank-1的识别率\（74.03 \％\）和\（71.32 \％\）将NIST SD27数据库中的258个样本与29,257和100,000个参考进行匹配，这是我们实验中使用的两个最大的参考数据库。