Oversized preliminary clusters were selected for

Oversized preliminary clusters were selected for subsequent splitting by agglomerative hierarchical clustering based on a criterion of (mean number of marked minutiae per examiner) >1.5. This arbitrary threshold was selected because (1) automated splitting of clusters meeting this criterion was highly successful, and (2) for lower values (between 1 and 1.5), it AG-14361 was usually not apparent even to a human how to split correctly without careful interpretation of the fingerprint image. The oversized preliminary clusters often contained multiple, clearly distinct ridge events, but otherwise were difficult to resolve by visual inspection. We used MATLAB׳s implementation of agglomerative hierarchical clustering algorithm; Ward׳s method was selected for computing the distance between clusters. Ward׳s method helps overcome the main flaw of DBSCAN, which is that it tends to fail when faced with highly heteroskedastic data (data in which the variance differs among subsets). Clustering was performed separately on Analysis markup (n=44,941 minutiae), Comparison markup (n=46,205 minutiae), and combined markup (n=52,155 minutiae). Combined markup (used in sections 9 and 10.2) includes both deleted and added minutiae. 94% of the Analysis-phase clusters have a maximum radius less than 1mm; 99.2% less than 1.5mm; 99.95% less than 2mm.
Minutia reproducibility and consensus (Analysis phase)
Reproducibility of nonminutia features Fig. 9 shows reproducibility of cores and deltas. Examiners were instructed to mark all cores and deltas on the latents, provided they could be located within approximately three ridge intervals. On those latents that had one or more cores or deltas marked by any examiners, typically only about half of the examiners marked them: no cores or deltas were unanimously marked. Table 10 shows the prevalence of nonminutia features in the area of minutia clusters. Features other than minutiae were sometimes present in or near minutia clusters, which could indicate a disagreement as to whether a feature should be marked as a minutia, a nonminutia feature, or both. However, Antiparallel did not explain much of the interexaminer variability: only 4.5% of clusters contained features other than minutiae.
Agreement in clarity markup (Analysis phase) Table 11 and Fig. 10 show for every minutia (n=44,941) the distribution of clarity assigned to that location by other examiners, regardless of whether the other examiners marked a minutia at that location. When an examiner marked a minutia in an area that that examiner described as unclear, other examiners were about equally likely to describe that area as clear or unclear. Table 12 and Fig. 11 show for every cluster center (n=10,324) the distribution of clarity assigned to that location by pairs of examiners, regardless of whether those examiners marked a minutia at that location. Selecting examiner pairs and cluster centers at random, the probability of the two examiners agreeing whether to describe that location as clear vs. unclear was 65%. Table 13 shows for every minutia marked (n=44,941) the distribution of clarity assigned to that location by other examiners, conditioned by whether the second examiner marked at that location. When a second examiner agreed on the presence of a minutia, that examiner was much more likely to describe the location as clear, whereas if the second examiner did not mark the minutia, that examiner was likely to describe the location as unclear.
Differences in regions with marked minutiae Some examiners mark minutiae far away from those marked by other examiners. This may be due to disagreements regarding the boundaries of the impression being considered (i.e., the region of interest), or disagreements on which areas in the region of interest are of sufficient quality to mark minutiae. Table 14 describes what proportion of minutiae were marked far from the nearest majority cluster. Fig. 12 (Analysis phase) and Fig. 13 (corresponding minutiae, Comparison phase) show the distributions of the distances from marked minutiae to the nearest majority cluster.