# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 4 Materials and Methods  
This section describes the database used and its characteristics. After, the two tested matching algorithms are presented. Finally, our experimental setup is presented. We used the database NIST SD27 [23], which is a public database widely used in latent fingerprint studies [13; 39]. NIST SD27 contains images of 258 latent crime scene fingerprints with their manually marked minutiae, as well as their matched tenprint impressions with their extracted minutiae. The average number of minutiae in latent fingerprint images in NIST SD27 is 20, while the average number of minutiae in the corresponding impressions is 106. Additionally, for some experiments, we used the NIST SD4 [40] as a background database. This database contains images of 2000 rolled fingerprints. For determining the impact of our study on fingerprint identification, we used two matching algorithms that have demonstrated highly competitive performance in latent fingerprint identification:  
* Minutia Cylinder-Code (MCC) [41]: Matching algorithm based on a three-dimensional representation constructed using basic minutiae features such as angles and distances to other minutiae. * Deformable Minutiae Clustering using Cylinder-Codes (DMCCC) [19]: A matching algorithm independent of minutiae descriptors based on the use of clustering to improve robustness to non-linear transformations. In this case, we use Minutia Cylinder-Code as the minutiae descriptor. We used CMC [42] curves as an evaluation measure adopting the norm ISO/IEC 19795-1, which indicates that CMC curves should be used for closed-set identification. As numerical measures of the CMC curve, we use from Rank-1 to Rank-100 identification rates. Notice that these ranks quantify the ratio of correct identifications in the first place and among the 100 first ranks, respectively, returned by an identification algorithm. Our protocol consisted of _three sets of experiments_ in which we removed minutiae from fingerprints and evaluated the impact of such minutiae removal on the two selected matching algorithms _plus a final prediction experiment_:  
1. The first set of experiments consists of randomly removing minutiae from latent fingerprints in the database and comparing the resulting CMC curves in a closed set comparison. We ran 35 experiments; the first 20 experiments consisted of removing a fixed number of minutiae from each latent fingerprint, from 1 to 20. The next 15 experiments consisted of removing a percent of minutiae from each latent fingerprint, from 0.1% to 0.75%. Each experiment was run 10 times with different randomly-selected removed minutiae, and the results of the 10 experiments are then averaged. 2. The second set of experiments aims to measure the matching algorithms' negative impact when minutiae are removed. We remove every possible combination of one, two, and three minutiae from every fingerprint (more than 1,100,000 combinations for all fingerprints in the NIST SD27) for this set of experiments. Every new fingerprint (with the removed minutiae) was tested with the two selected matching algorithms to obtain their matching score and compared with the matching score obtained from the original fingerprints (without removed minutiae). 3. The third set of experiments consists of determining the set of minutiae that less decreased the score and those that decreased the most the score. Using our second experiment results, we selected six combinations of minutiae for each fingerprint and each matching algorithm to see how the change in score is reflected in the CMC curve and the rank-100 identification. The three combinations which lowered the score the most are considered the \"lower\" class. The three combinations which lowered the score the least are considered the \"higher\" class. 4. Finally, we perform one last experiment aimed to determine if it is possible to predict if removing a minutia will have a positive or negative effect on the matching score.