# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 2 Previous Works
### Research on the Performance of Experts in Latent Fingerprint Analysis
[28] used 170 latent print examiners from which 89% got the qualification of latent print examiners. The dataset collected 320 fingerprint image pairs, 231 mated (from the same finger and person), and 89 nonmated (from different fingers or individuals). The image pairs were made with 301 latent fingerprints and 319 impressions. Each examiner was randomly assigned 17 mated fingerprint image pairs and 5 nonmated fingerprint image pairs. Their results were based on the analysis of 3730 fingerprint image pair, in total, among all the 170 latent print examiners, with a median of 12 examiners assigned to each fingerprint image pair. [PERSON] et al. [28] also proposed to analyze whether examiners agree on the inclusion or exclusion of minutiae by measuring the minor variations in minutia location. For doing that,  
Figure 2: Example markups of latent fingerprints taken from [PERSON] et al. [27]. On each latent fingerprint image, the retained minutiae are in yellow, moved in blue, deleted in red, and added in green. they used the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) [36] to group minutiae such that more than two examiners agreed they are the same on the latent fingerprint. In this way, as shown in Figure 3, clusters are labeled as a singleton (marked by only one examiner), minority (<50% of examiners), majority (50-90%), and supermajority (\(\geq\)90%). The results of [PERSON] et al. showed that 66% of minutiae were reproduced by most examiners, 67% of the singleton clusters were in unclear areas of the fingerprints, and 98% of supermajorities were clear areas. After an analysis, the authors identified several factors that affect that several examiners can reproduce the same minutia in a fingerprint: clarity, region of interest, feature type, and location. Authors also claimed that a difference in minutiae markup could be due merely to a difference in how an interpretation is documented by an examiner. Recently, in 2020, [PERSON] et al. studied the impact of evidence lineups on fingerprint expert decisions. Their experiment involved the participation of 43 latent fingerprint examiners, at an average age of 44 years, most of them affiliated to a large laboratory. In the experiment, each examiner viewed four fingerprints, some of which belonged to a suspect. Fingerprints came from a crime scene, visited by the examiners, as the underlying hypothesis of [PERSON] et al. is that an examiner could associate each latent fingerprint to a crime scene. Their results have shown that examiners made a positive identification of 37.8% for displayed images; by contrast, 25.6% images were classified as inconclusive. The main drawback of [29] is that examiners were aware of being part of a monitored study. This may explain why the study output an unusually high rate of inconclusive judgments attributed to suspicion of examiners aware they were under assessment. The study proposed by [PERSON] et al. is more for the implementation of evidence lineups than for analyzing the fingerprint from a dactyloscopy point of view. As a consequence, fingerprint features such as minutiae, pore, core, among others, were not analyzed. In summary, existing research has focused on studying human errors during the latent fingerprint markup. However, it has not considered the impact of missing minutiae on the score of matching algorithms or on the ranking results.