# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 5 Evaluating the Impact of Minutiae Errors
The six combinations for each fingerprint and matching algorithm were determined by taking the three combinations of minutiae that lowered the score the most (\"lower\" class) and the three combinations of minutiae that raised the most (\"higher\" class). Figure 8 shows a latent fingerprint taken from NIST SD27 [23], which contains the minutiae labeled as \"higher\" class in green color, those labeled as \"lower\" class in red color, and the remaining minutiae in blue color. From this figure, we can see that those minutiae labeled as \"lower\" class are well separated from those labeled as \"higher\" class. In addition, we can notice that the sum of the score of all \"lower\" class minutiae (\(-0.1728\)) affects more negatively the identification score than the sum of the score of all \"higher\" class minutiae (\(0.0665\)). Figure 7: CMC curves for random proportion removal of minutiae using both MCC and DMC as matching algorithms. (**a**) CMC curves for random proportion removal of minutiae using MCC as matching algorithm. (**b**) CMC curves for random proportion removal of minutiae using DMC as matching algorithm. Table 1 shows percentages in which there was a rank drop considering only those minutiae labeled as \"higher\" class. Table 2 shows percentages in which there was a rank drop taking into account only those minutiae labeled as \"lower\" class. Notice that for both tables, MCC obtained worse identification rates than DMC. In addition, in Table 2, the identification rates were most affected when those minutiae labeled as \"lower\" class were removed. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Num. Minutiae Removed** & **\#MCC** & **\#DMCCC** \\ \hline
1 & \(-5.53\%\) & \(-3.37\%\) \\
2 & \(-3.86\%\) & \(-0.69\%\) \\
3 & \(-3.86\%\) & \(-0.57\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Percentages in which there was a rank drop taking into account only those minutiae labeled with the class “higher.”  
Figure 8: Example of a latent fingerprint taken from NIST SD27 [23]. Minutiae labeled as “higher” class are in green color, those labeled as “lower” class are in red color, and the remaining minutiae are in blue color. In both “higher” and “lower” classes, the minutiae contain the score difference that they introduce to the final score when that particular minutia is removed. Our third set of experiments shows how the identification rate is affected when those minutiae labeled as \"lower\" class are removed. Figure 9 shows a histogram of rank loss of MCC when removing the minutiae of the \"lower\" class. From this figure, we can notice how removing minutiae labeled as \"lower\" class can affect the MCC rank value with variations up to 2000 positions in a ranked list. It means that a fingerprint ranked in the top 50 positions can easily drop to top 1000. These results demonstrate the high impact of these errors in the performance of fingerprint identification systems. In this figure, we can see, as expected, that the more minutiae are removed, the higher the rank loss. Notice that when only two or three minutiae are missed, the drop is severe for a large number of fingerprints. Figure 10 shows a histogram of rank loss regarding DMC when \"lower\" class minutiae are removed. Contrasting the results obtained by Figures 9 and 10, we can see that the matching algorithm DMC is less affected than MCC when minutiae labeled as \"lower\" class are removed. However, notice that when two or three minutiae are removed the histogram is still highly affected.