# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 5 Evaluating the Impact of Minutiae Errors  
This section shows the experimental results obtained when removing fixed and variable numbers of random minutiae from each latent fingerprint. For our first set of experiments, Figure 5 shows CMC curves for random fixed number removal of minutiae using both MCC and DMC as matching algorithms. From this figure, we can see how both matching algorithms degrade for the increasing number of minutiae removed. In addition, notice that MCC obtains worse identification rates than DMC regarding each CMC curve plotted. From these results, we can see that no matter the matching algorithm used, the identification rate is affected when minutiae are randomly removed from latent fingerprints. The drop of performance varies depending on the matching algorithm, but it is consistently high with rank-n identification degradation higher than 5% for each minutia removed. Figure 6 shows the same results from Figure 5 in a different representation. From Figure 6, we notice that the identification rate decay proportionally to the number of minutiae removed. We can also see that rank one is more affected when minutiae are removed than the remaining ranks (from 2 to 6). In addition, we can notice that MCC's identification rates are more affected than those of DMC regarding the number of minutiae removed and the ranks analyzed. From our first set of experiments, we noted that the identification rate depends on both the number of removed minutiae and the total number of minutiae. Consequently, another essential experiment is to test removing a percentage of minutiae from each latent fingerprint. For our second set of experiments, Figure 7 shows CMC curves for random proportion removal of minutiae using both MCC and DMC as matching algorithms. From this figure, notice that the identification rate decays very fast, with rank-n identification rate differences of 12% for a ratio of 0.2 missed minutiae. In addition, from Figure 7, we can see that the identification rate obtained by MCC (Figure 7a) is worse than the identification rate obtained by DMC (Figure 7b). These results suggest that the characteristics of the matching algorithm can serve to alleviate this drop in performance partially. Figure 5: CMC curves for random fixed number removal of minutiae using both MCC and DMC as matching algorithms. (**a**) CMC curves for random fixed number removal of minutiae using MCC as matching algorithm. (**b**) CMC curves for random fixed number removal of minutiae using DMC as matching algorithm. Figure 6: Curves showing the identification rates of both MCC and DMC matching algorithms when a random number of minutiae are removed. (**a**) Curves showing the MCC matching algorithm’s identification rates when a random number of minutiae is removed. (**b**) Curves showing the DMC matching algorithm’s identification rates when a random number of minutiae is removed. From our second set of experiments, we noted that some minutiae affected the matching scores differently than others. In addition, on the one hand, \(2/3\) of the minutiae lowered the matching score when removed. On the other hand, \(1/3\) of the minutiae have a null effect or even a small improvement of the matching score when removed when compared to the base score (no minutiae removed). From the results obtained in our second set of experiments, we concluded that some minutiae are more critical for identification, but we still do not know why they are more important than others. The next experiment is performed to characterize minutiae that lower the matching score and compare them to the remaining minutiae to determine the reasons for this negative impact. Using the results of our second set of experiments, we selected the 6 minutiae combinations of sizes one, two, and three that had the largest impact in matching score for each latent fingerprint and each matching algorithm to see how the change in scores reflected in the CMC curve and from the rank-1 to rank-100 identification.