# Impact of Minutiae Errors in Latent Fingerprint Identification: Assessment and Prediction
## 6 Predicting the Impact of Minutiae Errors  
In the previous section, we showed that removing minutiae can decrease or increase the score of fingerprint matching algorithms. If we could automatically predict the impact of the minutiae in the score before performing latent fingerprint identification, we could: (1) help the forensics experts to improve their minutiae labeling process to maximize the probability that they find minutiae that lead to successful latent fingerprint identification [16], and (2) improve automated algorithms for latent fingerprint processing and recognition using quality-based processing [44] and quality-based information fusion [45; 46; 47]. With these goals in mind, we developed a framework to determine if it is possible to predict if a given minutia will have a positive or negative effect on the score of fingerprint matching. Thus, we could develop tools for forensics examiners to automatically classify regions by its impact on the performance of the identification algorithm, or quality-based algorithms for latent fingerprint processing [45; 46]. This framework was created by using the results of removing every possible combination of one minutia from a fingerprint, as described in the previous section for the DMC matcher. We created features based on the location of each minutia and the distances of neighboring minutiae. We designed two different types of features: (1) the first type counts the number of minutiae in a radius neighboring the specified minutia, and (2) the second type is the distance from the specified minutia to the \(n\) closest minutia. Finally, the class values _positive_ and _negative_ were used for minutiae that, after being removed, increased or decreased the matching score, respectively. In the end, each datum point in the created dataset has the following information:  
* d[1; 2; 3; 4; 5; 6]: Each of these features has the distance from the minutia to the closest minutia (d[1]), to the second closest minutia (d[2]), and so on. * r[15; 30; 45; 60; 75; 90]: Each of these features counts how many minutiae there are in a radius (of 15 pixels, 30 pixels, and so on) around the specified minutia. Figure 10: Histogram of rank loss for “lower” class regarding DMC. class: Either a positive or negative impact on the matching score. In total, this dataset has 12 features and a class feature. It contains 5215 instances, of which 1991 are positive examples (minutiae that increase the matching score when removed) and 3224 are negative examples (minutiae that lower the matching score when removed). We tested eight different supervised classifiers. We selected popular classifiers of different types to get varied results. For each algorithm, we did five-fold distribution balanced stratified cross-validation [48], which is used for imbalanced class problems. We evaluated each algorithm with the area under the receiver operating characteristic curve (AUC) [49]. The AUC is calculated as:  
\[\frac{1+\text{TPR}-\text{FPR}}{2} \tag{1}\]  
where TPR is true positive rate and FPR is false positive rate. The eight supervised classifiers that we used were:  
* Fisher's Linear Discriminant (LDA). * Quadratic Discriminant Analysis (QDA). * PBC4 cip [50]. * Random Forest [51]. * Bagging [52]. * Logistic Regression [53]. * Multilayer Perceptron. * Support Vector Machines (SVM) [54]. Figure 11 shows a bar graph comparing the average AUC of each supervised classifier when using five-fold distribution balanced stratified cross-validation. From Figure 11, we can see that PBC4 cip [50] has the best performance with an AUC of.608, closely followed by FLDA and Random Forest [51]. The accuracy is still low, but these results suggest the existence of certain information that can be exploited to predict if a minutia will increase or decrease the matching score of a fingerprint with a better than random AUC. These results open a new research line related to creating automatic algorithms that help forensics experts to improve the minutiae labeling process in latent fingerprints.