br Consensus and sufficiency Analysis and comparison phases

Consensus and sufficiency (Analysis and comparison phases) Previously, we reported [2] that the number of minutiae annotated by examiners is strongly associated with their own value and comparison determinations, and that seven minutiae was an approximate “tipping point”: “for any minutia count greater than seven, the majority of value determinations were VID, and for any corresponding minutia count greater than seven, the majority of comparison determinations were individualization.” Across multiple examiners, a mean of seven corresponding minutiae was also the point at which approximately 50% of examiners individualized (approximately 50% of examiners assessed latents to be VID when the mean minutia count was seven). Here we report similar thresholds as measured by consensus on minutia clusters. We find counts of majority clusters comparable to mean minutia counts as predictors of examiner determinations. For example, when predicting VID determinations using logistic regression, r2=0.4253 for mean minutia counts vs. r2=0.4310 for majority clusters. As shown in Fig. 14, these majority cluster statistics are highly correlated with the mean number of minutiae, which tends to be slightly larger than the number of majority clusters. As shown in Figs. 15 and 16A, latents with fewer than 5 majority clusters were usually not assessed as VID; latents with 10 or more majority clusters were usually assessed to be VID. Fig. 16B shows a similar association for clusters corresponded by the majority of comparing examiners: almost all image pairs with 7 or more clusters that were corresponded by a majority of comparing examiners were individualized by the majority of examiners; almost no image pairs with 5 or fewer majority corresponding clusters were individualized by the majority of examiners. In [2] we included several figures to show the association between minutia counts and value determinations, and between corresponding minutia counts and comparison determinations. Fig. 17 is comparable to Fig. 5 of [2] except that it purchase AG-14361 includes a data series for the number of clusters corresponded by a majority of examiners who compared the image pair; it also includes data for both mated and nonmated image pairs. In general, the number of majority clusters tends to be approximately equal to the mean minutia count.
Reproducibility of analysis-comparison changes As previously reported, examiners often modified their latent Analysis markup during the Comparison phase [7]. For each pair of latent markups (analysis and comparison phases), we classified features as retained, moved, deleted, or added. A retained feature is one calcitonin is present at exactly the same pixel location in both markups; a moved feature refers to one that was deleted during Comparison and replaced by another within 0.5mm (approximately one ridge width); a deleted feature is one that was present in the Analysis markup only (no Comparison feature within 0.5mm); an added feature is one that was present in the Comparison markup only (no Analysis feature within 0.5mm). Fig. 18 summarizes the extent of such changes, by clarity, showing that unclear minutiae were much more likely to be changed. Tables 15 and 16 show that deleted and added minutiae are strongly associated with low reproducibility. This association is stronger in clear areas than unclear areas: using logistic regression to predict deletions and additions from minutia reproducibility, we find that for deleted minutiae, r2=0.1243 (clear) and 0.0686 (unclear); for added minutiae, r2=0.0640 (clear) and 0.0332 (unclear). Having shown that reproducibility and clarity are strongly associated, we took a closer look at how reproducibility and clarity are associated with changes. We used logistic regression to model deleted and added minutiae as responses to reproducibility and clarity. Predicting deleted minutiae from reproducibility and examiner clarity (r2=0.1114), only the reproducibility term is significant; clarity provides no additional information (using median clarity makes no meaningful improvement to the model: r2=0.1116). Predicting added minutiae from reproducibility and examiner clarity (r2=0.0762), both terms are significant, though the reproducibility term contributes much more than clarity (predicting added minutiae from reproducibility alone results in r2=0.0682; from examiner clarity alone, r2=0.0271; from median clarity alone, r2=0.0359). Examiners are more likely to add minutiae in low-clarity areas even after accounting for reproducibility of those minutiae. Our ability to predict deleted minutiae is not further improved by knowing clarity after accounting for reproducibility.