# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 2 Previous Works
### Impact of Fingerprint Variations in Automatic Fingerprint Recognition  
In this section, we present an analysis on the performance of fingerprint identification systems in the presence of different distortions.  
Studying the impact of fingerprint variations on fingerprint identification has attracted some attention in the last two decades, including position variability [35] and other distortions [10].  
The most up-to-date and comprehensive work in this regard may be [PERSON] et al. [37], who performed a white-box evaluation of three fingerprint matchers so as to assess how different perturbations in fingerprints affect the matchers' scores. Of these algorithms, one, called SourceAFIS, was open-source and the other two were unnamed, minutiae-based  
Figure 3: Two examples of latent fingerprints marked by several examiners, taken from [28]. Marked minutiae are shown as small black dots inside color-coded clusters. Singleton (marked by only one examiner), minority (<50% of examiners), majority (50–90%), and supermajority (\(\geq\)90%).  
commercial-off-the-shelf (COTS) matchers. Authors evaluated the robustness of these matchers against controlled perturbations of the fingerprints, specifically perturbations of minutiae sets. The fingerprint dataset used in this experimentation consists of fingerprint impressions, synthetically generated through SFinGe [38]. It is composed of 5000 different \"master\" fingerprints, each of which was used to generate two different impressions. For performance evaluation, [PERSON] et al. also generated a ground-truth minutiae set for each fingerprint.  
Authors ran experiments where fingerprint impressions involved the following perturbations: (i) moving and rotating minutiae; (ii) adding spurious minutiae and removing ground-truth minutiae; (iii) considering a non-linear distortion of the minutiae sets; and (iv) combining the displacement and rotation of minutiae with the addition of spurious minutiae and removal of ground truth minutiae. The first two perturbations were generated by using a multivariate Gaussian Distribution model, which had parameters the researchers considered to model real possible perturbations. The non-linear perturbations were generated by using a non-linear distortion model learned from distorted fingerprints. Using these distortions, the authors performed two different experiments: (a) an analysis of uncertainty resulting from realistic amounts of perturbation and distortion; and (b) an evaluation of the recognition performance of each minutia-based matcher on increasing levels of perturbation and distortion.  
To perform the uncertainty analysis, [PERSON] et al. generated 100 perturbations per perturbation type, per generated fingerprint. They obtained similarity scores and used them to calculate a global uncertainty score relating to each perturbation type. When performing the second experiment, the authors increased the perturbation parameters throughout eight iterations. The results of this experiment are reported as true acceptance rate at a fixed false acceptance rate of 0.01%. They also calculated impostor scores for each perturbation type, on each iteration. The authors found out that the non-linear distortion is the perturbation that reduced the mean of the similarity scores the greatest, followed by the combined perturbation, and after that, the removal of ground truth minutiae. The experiments suggested that the fingerprint identification algorithms are robust to elevate percentages of missing minutiae. The large number of minutiae in these experiments allows to maintain a competitive identification performance after missing minutiae.