# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 2 Previous Works
### Research on the Performance of Experts in Latent Fingerprint Analysis  
In this sub-section, we present works that study the performance of human latent fingerprint examiners when marking or matching latent fingerprints. These works are intended to show that even experts can make mistakes and serve as the motivation for our research on the effects of mistakes in this area. In 2011, [PERSON] et al. studied the accuracy and reliability of forensic latent fingerprint decisions by using empirical approaches [26]. In particular, they studied how the quantity and quality of image features relate to the level of consensus among examiners and their decisions. The study of [PERSON] et al. was focused on determining the frequency of false-positive and false-negative errors, the extent of consensus among examiners, and factors contributing to variability in results. In such a study, 169 latent print examiners participated, who were generally highly experienced (83% of the participants were certified as latent print examiners). The data included 356 latent fingerprints, from 165 distinct fingers from 21 people, and 484 impressions combined finally making up 744 distinct latent-impression image pairs. [PERSON] et al. balanced the number of fingerprint pairs used in their study and the number of examiners assigned to each pair. As a result, 100 image pairs from the 744 image pairs were assigned to each participant examiner. The results of [PERSON] et al. showed that, on the one hand, the false positive rate error (FPR) was 0.1%, and never two examiners committed an error on the same comparison. These results indicate that blind verification (between two examiners) should be highly effective at detecting this type of error, which demonstrates the potential of information fusion approaches in this problem [33; 34]. On the other hand, the false-negative error was much more frequent (7.5% of mated comparisons). An in-depth analysis detected that 85% of the examiners committed at least one false-negative error. Using a blind verification would detect most of the false-negative errors; however, this blind verification is not generally practiced in operational procedures. After studying the quality of the latent fingerprints (quantity of features, distortion, background, among others) [18; 35], this study could not identify the prints' features associated with false positive or false negative errors. Later on, in 2015, [PERSON] et al. analyzed the changes in latent fingerprint examiners' markup [27]. The authors collected 320 (231 mated and 89 nonmated) image pairs of fingerprints constructed from 301 latent fingerprints and 319 impressions. Each one of the 170 volunteer latent print examiners was randomly assigned to analyze 22 pairs of fingerprints. From the 170 participants of this study, 89% were qualified as latent print examiners. The experiments executed by [PERSON] et al. showed that from 41,774 minutiae labeled, 87% were retained, 6% were moved, 7% deleted, and 17% were added, see Figure 2. From these reports, the authors concluded that the comparison rates for inclusion or exclusion of minutiae ranged from 14% to 54%. A detailed report of [PERSON] et al. showed that most of the latent fingerprint examiners (86%) included or excluded minutiae in the majority of their comparisons. When examiners were individualized (without checking by using double examiners), they changed their markup of minutiae in most comparisons. In addition, examiners included minutiae less frequently when the fingerprint image pair was nonmated than mated, being a possible explanation that the comparison with a mated exemplar draws attention to additional corresponding features in the latent. The authors claimed that the high variation in rates could be due to participants' unfaniliarity with the tested tools as well as the instructions and bringing casework habits. Next, in 2016, [PERSON] et al. analyzed the interexaminer variation of minutia markup on latent fingerprints [28]. Similar to [27], [PERSON] et al.